{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "479c3419",
   "metadata": {},
   "source": [
    "### Library Type Breakdown\n",
    "This portion of the data analysis focuses on library types to analyze whether certain library types experiences more drastic changes over others. \n",
    "\n",
    "Libraries Type data was extracted from the master Impact_of_COVID_on_Tech_Services_Clean_3.CSV sheet. Library types were originally sorted in Excel to include the dominant library types (Academic, Public, K-12, Special, Other, and Blanks), which may have included sub-categories, such as Academic libraries housing special collection or other library types within their institution or physical building. Data was re-sorted on 02/23/2022 to group responses into categories by inclusion of any library type keyword in the breakdown spreadsheets (e.g. A library self-reporting being Academic, Special, and Other would be represented in each of these breakdown spreadsheets to better reflect the cohesive trends happening in these larger institutions impacting their subsideary libraries).  \n",
    "\n",
    "Data in staffing numbers columns were standardized to count total number of FT/PT staff and to remove any string text/uncertain values. Rows with NaN data will be removed when analyzing these columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d541bbc7",
   "metadata": {},
   "source": [
    "Links to related Python script for this study: \n",
    "\n",
    "COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data): https://doi.org/10.7910/DVN/SXMSDZ\n",
    "COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type): https://doi.org/10.7910/DVN/SXMSDZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f4fae905",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setup Numpy and Pandas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Import Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c1cea1a3",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Q1         Q2              Q3   Q4  \\\n",
      "0  Yes  As-Needed  1 Year or more  Yes   \n",
      "1  Yes  As-Needed  1 Year or more  Yes   \n",
      "2  Yes  As-Needed  1 Year or more  Yes   \n",
      "3  Yes        Yes  1 Year or more  Yes   \n",
      "4  Yes        Yes  1 Year or more  Yes   \n",
      "\n",
      "                                                  Q5    Q6    Q7  \\\n",
      "0  Cataloging,Processing,Electronic Resources,Acq...   6.0   7.0   \n",
      "1  Cataloging,Processing,Federal Depository Libra...   8.0   5.0   \n",
      "2  Cataloging,Processing,Electronic Resources,Acq...   6.0   5.0   \n",
      "3  Cataloging,Processing,Electronic Resources,Acq...  40.0  40.0   \n",
      "4                    Cataloging,Electronic Resources   9.0   9.0   \n",
      "\n",
      "                                                  Q8   Q9           Q10  ...  \\\n",
      "0                               Voluntary Retirement  Yes  I'm not sure  ...   \n",
      "1         Furloughs,Voluntary Retirement,Resignation   No           Yes  ...   \n",
      "2                               Voluntary Retirement   No           Yes  ...   \n",
      "3                               Voluntary Retirement  Yes            No  ...   \n",
      "4  We did not temporarily or permanently lose sta...  NaN            No  ...   \n",
      "\n",
      "                                                 Q29  Q30  \\\n",
      "0  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "1   Change to work environment (e.g. work-from-home)  NaN   \n",
      "2  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "3   Change to work environment (e.g. work-from-home)  NaN   \n",
      "4                                                NaN  NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0  I strongly agree that these changes have been ...   \n",
      "1  I somewhat agree that these changes have been ...   \n",
      "2  I somewhat agree that these changes have been ...   \n",
      "3  I strongly agree that these changes have been ...   \n",
      "4                                     Not applicable   \n",
      "\n",
      "                                                 Q32                 Q33  \\\n",
      "0  I strongly agree that these changes will have ...  Extremely positive   \n",
      "1  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "2  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "3  I strongly agree that these changes will have ...  Extremely positive   \n",
      "4                                     Not applicable      Not applicable   \n",
      "\n",
      "                                                 Q34  Q35             Q36  \\\n",
      "0                                                NaN   No             NaN   \n",
      "1  The institution has been more flexible as a wh...  NaN             NaN   \n",
      "2                                                NaN   No         Georgia   \n",
      "3                                                NaN   No  North Carolina   \n",
      "4                                                NaN   No           Texas   \n",
      "\n",
      "                Q37                             Q38  \n",
      "0  Academic Library                       Librarian  \n",
      "1  Academic Library  Manager, Supervisor, Unit Head  \n",
      "2  Academic Library                       Librarian  \n",
      "3  Academic Library                       Librarian  \n",
      "4  Academic Library                       Librarian  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "      Q1         Q2              Q3   Q4  \\\n",
      "256  Yes  As-Needed  1 Year or more  Yes   \n",
      "257  Yes        Yes  1 Year or more  Yes   \n",
      "258  Yes        Yes  1 Year or more  Yes   \n",
      "259  Yes        Yes  1 Year or more  Yes   \n",
      "260  Yes  As-Needed      4-6 Months  Yes   \n",
      "\n",
      "                                                    Q5    Q6   Q7  \\\n",
      "256  Cataloging,Processing,Electronic Resources,Int...   3.5  3.5   \n",
      "257  Cataloging,Library Systems (e.g. Discovery or ...   6.0  5.0   \n",
      "258  Cataloging,Processing,Electronic Resources,Acq...  13.0  8.0   \n",
      "259  Processing,Electronic Resources,Acquisitions,C...   7.0  7.0   \n",
      "260         Cataloging,Processing,Electronic Resources   3.0  3.0   \n",
      "\n",
      "                                                    Q8   Q9             Q10  \\\n",
      "256  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "257                     Furloughs,Voluntary Retirement   No    I'm not sure   \n",
      "258                      Lay-Offs,Voluntary Retirement  Yes             Yes   \n",
      "259  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "260  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "\n",
      "     ...                                                Q29  \\\n",
      "256  ...  Change to Acquisitions, Collection Development...   \n",
      "257  ...                                                NaN   \n",
      "258  ...   Change to work environment (e.g. work-from-home)   \n",
      "259  ...  Change to Acquisitions, Collection Development...   \n",
      "260  ...             Change to Cataloging and/or Processing   \n",
      "\n",
      "                                                   Q30  \\\n",
      "256                                                NaN   \n",
      "257                                                NaN   \n",
      "258                                                NaN   \n",
      "259  We are all working hybrid schedules where we a...   \n",
      "260                                                NaN   \n",
      "\n",
      "                                                   Q31  \\\n",
      "256  I somewhat agree that these changes have been ...   \n",
      "257                                     Not applicable   \n",
      "258  I somewhat agree that these changes have been ...   \n",
      "259  I strongly agree that these changes have been ...   \n",
      "260  I somewhat agree that these changes have been ...   \n",
      "\n",
      "                                                   Q32                 Q33  \\\n",
      "256  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "257                                     Not applicable      Not applicable   \n",
      "258  I strongly agree that these changes will have ...   Somewhat positive   \n",
      "259  I strongly agree that these changes will have ...  Extremely positive   \n",
      "260  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "\n",
      "                                                   Q34 Q35           Q36  \\\n",
      "256  College policies will ultimately dictate how r...  No       Indiana   \n",
      "257                                                NaN  No      New York   \n",
      "258                                                NaN  No          Ohio   \n",
      "259  I worry that as time goes by, and with adminis...  No  Rhode Island   \n",
      "260                                                NaN  No      Illinois   \n",
      "\n",
      "                  Q37                             Q38  \n",
      "256  Academic Library  Manager, Supervisor, Unit Head  \n",
      "257  Academic Library  Manager, Supervisor, Unit Head  \n",
      "258  Academic Library  Manager, Supervisor, Unit Head  \n",
      "259  Academic Library  Manager, Supervisor, Unit Head  \n",
      "260  Academic Library  Manager, Supervisor, Unit Head  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Academic Libraries\n",
    "\n",
    "#Import File\n",
    "al=pd.read_csv('Impact_of_COVID_on_Tech_Services_Academic.csv')\n",
    "\n",
    "#Data Analysis\n",
    "print(al.head()) #Column names, data samples\n",
    "print(al.tail()) #Tail sample, 261 rows\n",
    "al.describe() #5 rows with 38 columns\n",
    "type(al)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2d2900e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   0\n",
      "Cataloging                                       245\n",
      "Processing                                       207\n",
      "Electronic Resources                             183\n",
      "Acquisitions                                     179\n",
      "Library Systems (e.g. Discovery or ILS support)  112\n",
      "Collection Development                            94\n",
      "Archives and/or Special Collections               67\n",
      "Federal Depository Library Program                53\n",
      "Interlibrary Loan                                 51\n",
      "Other                                             23\n"
     ]
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "al['Q5'] = al['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "al_areas=al['Q5'].str.split(',', expand=True).stack().value_counts().to_frame()\n",
    "print(al_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f1de17a7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "academic_df={'Function':['Cataloging', 'Processing', \n",
    "                        'Electronic Resources', 'Acquisitions',\n",
    "                        'Library Systems', 'Collection Development',\n",
    "                        'Archives and/or Special Collections', \n",
    "                        'Federal Depository Library Program', \n",
    "                        'Interlibrary Loan', 'Other'], 'Number of Units Including Functions': [245, 207, 183, 179, 112, 94, 67, 53, 51, 23]}\n",
    "\n",
    "x=academic_df['Function']\n",
    "y=academic_df['Number of Units Including Functions']\n",
    "labels=['Cataloging', 'Processing', 'Electronic Resources', \n",
    "        'Acquisitions','Library Systems', 'Collection Development', \n",
    "        'Archives and/or Special Collections', \n",
    "        'Federal Depository Library Program', \n",
    "        'Interlibrary Loan', 'Other']\n",
    "\n",
    "plt.pie(y, labels=labels, autopct='%1.1f%%')\n",
    "plt.title('Technical Services Participation by Academic Libraries')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Academic_Services.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "16df97f7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q6    Q7\n",
       "0     6.0   7.0\n",
       "1     8.0   5.0\n",
       "2     6.0   5.0\n",
       "3    40.0  40.0\n",
       "4     9.0   9.0\n",
       "..    ...   ...\n",
       "256   3.5   3.5\n",
       "257   6.0   5.0\n",
       "258  13.0   8.0\n",
       "259   7.0   7.0\n",
       "260   3.0   3.0\n",
       "\n",
       "[255 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Staffing Numbers Before and After\n",
    "al_staffing=al[['Q6','Q7']]\n",
    "al_staffing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "08e67726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.547665369649805\n",
      "6.61078431372549\n",
      "1939.75\n",
      "1685.75\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(al_staffing['Q6'].mean())\n",
    "print(al_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 7.5\n",
    "# After: 6.6\n",
    "# Percent Change: 12% Decrease\n",
    "\n",
    "#Sum\n",
    "print(al_staffing['Q6'].sum())\n",
    "print(al_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 1939.75\n",
    "# After: 1685.75\n",
    "# Difference: 254 positions lost\n",
    "# Percent Change: 13.1% Decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fe135edb",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Q1         Q2                    Q3   Q4  \\\n",
      "0  Yes         No            4-6 Months   No   \n",
      "1  Yes  As-Needed        1 Year or more  Yes   \n",
      "2   No         No        Not applicable   No   \n",
      "3  Yes         No  Three Months or less   No   \n",
      "4  Yes  As-Needed  Three Months or less  Yes   \n",
      "\n",
      "                                                  Q5    Q6    Q7  \\\n",
      "0                 Cataloging,Processing,Acquisitions   8.0   9.0   \n",
      "1                                         Cataloging   NaN   NaN   \n",
      "2                 Cataloging,Processing,Acquisitions  11.0  11.0   \n",
      "3  Cataloging,Processing,Acquisitions,Collection ...   3.0   3.0   \n",
      "4                              Cataloging,Processing  10.0  10.0   \n",
      "\n",
      "                                                  Q8   Q9             Q10  \\\n",
      "0  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "1                               Voluntary Retirement   No              No   \n",
      "2  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "3  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "4  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "\n",
      "   ...                                                Q29  Q30  \\\n",
      "0  ...                               Change to a workflow  NaN   \n",
      "1  ...   Change to work environment (e.g. work-from-home)  NaN   \n",
      "2  ...                                                NaN  NaN   \n",
      "3  ...                                                NaN  NaN   \n",
      "4  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0  I strongly agree that these changes have been ...   \n",
      "1  I strongly agree that these changes have been ...   \n",
      "2                                     Not applicable   \n",
      "3                                     Not applicable   \n",
      "4  I somewhat agree that these changes have been ...   \n",
      "\n",
      "                                                 Q32                 Q33  Q34  \\\n",
      "0  I somewhat agree that these changes will have ...   Somewhat positive   no   \n",
      "1  I neither agree nor disagree on the impact tha...  Extremely positive  NaN   \n",
      "2                                     Not applicable      Not applicable  NaN   \n",
      "3                                     Not applicable      Not applicable  NaN   \n",
      "4  I somewhat agree that these changes will have ...   Somewhat positive  NaN   \n",
      "\n",
      "  Q35         Q36             Q37                             Q38  \n",
      "0  No    Illinois  Public Library  Manager, Supervisor, Unit Head  \n",
      "1  No         NaN  Public Library                       Librarian  \n",
      "2  No  Washington  Public Library                       Librarian  \n",
      "3  No        Ohio  Public Library                       Librarian  \n",
      "4  No       Texas  Public Library                       Librarian  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "            Q1         Q2              Q3   Q4  \\\n",
      "233        Yes  As-Needed      4-6 Months  Yes   \n",
      "234  As-Needed  As-Needed  1 Year or more  Yes   \n",
      "235         No         No  Not applicable   No   \n",
      "236         No         No  Not applicable  NaN   \n",
      "237        Yes        Yes  1 Year or more  NaN   \n",
      "\n",
      "                                                    Q5    Q6    Q7  \\\n",
      "233  Cataloging,Processing,Archives and/or Special ...  28.0  25.0   \n",
      "234                              Cataloging,Processing   9.0  10.0   \n",
      "235  Cataloging,Processing,Electronic Resources,Int...  12.0  12.0   \n",
      "236  Cataloging,Processing,Archives and/or Special ...  10.0  10.0   \n",
      "237  Cataloging,Electronic Resources,Acquisitions,L...   2.5   2.5   \n",
      "\n",
      "                                                    Q8            Q9  \\\n",
      "233                   Voluntary Retirement,Resignation           Yes   \n",
      "234                                          Furloughs  I'm not sure   \n",
      "235  We did not temporarily or permanently lose sta...           NaN   \n",
      "236  We did not temporarily or permanently lose sta...           NaN   \n",
      "237  We did not temporarily or permanently lose sta...           NaN   \n",
      "\n",
      "                Q10  ...                                                Q29  \\\n",
      "233              No  ...  Change to Cataloging and/or Processing,Change ...   \n",
      "234              No  ...   Change to work environment (e.g. work-from-home)   \n",
      "235    I'm not sure  ...                                                NaN   \n",
      "236              No  ...                                                NaN   \n",
      "237  Not applicable  ...  Change to a workflow,Change to documentation,C...   \n",
      "\n",
      "     Q30                                                Q31  \\\n",
      "233  NaN  I strongly agree that these changes have been ...   \n",
      "234  NaN  I neither agree nor disagree about the success...   \n",
      "235  NaN  I neither agree nor disagree about the success...   \n",
      "236  NaN                                     Not applicable   \n",
      "237  NaN  I strongly agree that these changes have been ...   \n",
      "\n",
      "                                                   Q32  \\\n",
      "233  I strongly agree that these changes will have ...   \n",
      "234  I neither agree nor disagree on the impact tha...   \n",
      "235  I neither agree nor disagree on the impact tha...   \n",
      "236                                     Not applicable   \n",
      "237  I strongly agree that these changes will have ...   \n",
      "\n",
      "                               Q33  \\\n",
      "233              Somewhat positive   \n",
      "234  Neither positive nor negative   \n",
      "235                 Not applicable   \n",
      "236                 Not applicable   \n",
      "237             Extremely positive   \n",
      "\n",
      "                                                   Q34 Q35       Q36  \\\n",
      "233  There is concern that since we are outsourcing...  No  Maryland   \n",
      "234                                                NaN  No  Colorado   \n",
      "235                                                NaN  No  Oklahoma   \n",
      "236                                                NaN  No   Florida   \n",
      "237                                               Nope  No    Oregon   \n",
      "\n",
      "                Q37                                             Q38  \n",
      "233  Public Library                  Manager, Supervisor, Unit Head  \n",
      "234  Public Library                                       Librarian  \n",
      "235  Public Library  Library Assistant, Associate, Paraprofessional  \n",
      "236  Public Library                  Manager, Supervisor, Unit Head  \n",
      "237  Public Library                                       Librarian  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Public Libraries\n",
    "\n",
    "#Import File\n",
    "pub=pd.read_csv('Impact_of_COVID_on_Tech_Services_Public.csv')\n",
    "\n",
    "#Data Analysis\n",
    "print(pub.head()) #Column names, data samples\n",
    "print(pub.tail()) #Tail sample, 238 rows\n",
    "pub.describe() #5 rows with 38 columns\n",
    "type(pub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ff1e6f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   0\n",
      "Cataloging                                       230\n",
      "Processing                                       218\n",
      "Acquisitions                                     182\n",
      "Collection Development                           123\n",
      "Electronic Resources                             106\n",
      "Interlibrary Loan                                 95\n",
      "Library Systems (e.g. Discovery or ILS support)   71\n",
      "Archives and/or Special Collections               49\n",
      "Other                                             17\n",
      "Federal Depository Library Program                 5\n"
     ]
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "pub['Q5'] = pub['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "pub_areas=pub['Q5'].str.split(',', expand=True).stack().value_counts().to_frame()\n",
    "print(pub_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "edd94110",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "public_df={'Function':['Cataloging', 'Processing', \n",
    "                        'Acquisitions', 'Collection Development',\n",
    "                        'Electronic Resources', 'Interlibrary Loan',\n",
    "                        'Library Systems', \n",
    "                        'Archives and/or Special Collections', \n",
    "                        'Other', 'Federal Depository Library Program'], 'Number of Units Including Functions': [230, 218, 182, 123, 106, 95, 71, 49, 17, 5]}\n",
    "\n",
    "x=public_df['Function']\n",
    "y=public_df['Number of Units Including Functions']\n",
    "labels=['Cataloging', 'Processing', \n",
    "        'Acquisitions', 'Collection Development',\n",
    "        'Electronic Resources', 'Interlibrary Loan',\n",
    "        'Library Systems', 'Archives and/or Special Collections', \n",
    "        'Other', 'Federal Depository Library Program']\n",
    "\n",
    "plt.pie(y, labels=labels, autopct='%1.1f%%', pctdistance=0.85, textprops={'fontsize': 8})\n",
    "plt.title('Technical Services Participation by Public Libraries')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Public_Services.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "971f65e9",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q6    Q7\n",
       "0     8.0   9.0\n",
       "2    11.0  11.0\n",
       "3     3.0   3.0\n",
       "4    10.0  10.0\n",
       "5     3.0   3.0\n",
       "..    ...   ...\n",
       "233  28.0  25.0\n",
       "234   9.0  10.0\n",
       "235  12.0  12.0\n",
       "236  10.0  10.0\n",
       "237   2.5   2.5\n",
       "\n",
       "[229 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Staffing Numbers Before and After\n",
    "pub_staffing=pub[['Q6','Q7']]\n",
    "pub_staffing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d2f8a2ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.04004329004329\n",
      "6.592173913043479\n",
      "1626.25\n",
      "1516.2\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(pub_staffing['Q6'].mean())\n",
    "print(pub_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 7.0\n",
    "# After: 6.6\n",
    "# Percent Change: 5.71% Decrease\n",
    "\n",
    "#Sum\n",
    "print(pub_staffing['Q6'].sum())\n",
    "print(pub_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 1626.25\n",
    "# After: 1516.2\n",
    "# Difference: 110.05 positions lost\n",
    "# Percent change: 6.8% decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7f4270a1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Q1  Q2                    Q3   Q4  \\\n",
      "0        Yes  No  Three Months or less   No   \n",
      "1  As-Needed  No            4-6 Months   No   \n",
      "2         No  No        Not applicable  NaN   \n",
      "3        Yes  No  Three Months or less  NaN   \n",
      "4  As-Needed  No  Three Months or less   No   \n",
      "\n",
      "                                                  Q5   Q6   Q7  \\\n",
      "0  Cataloging,Processing,Acquisitions,Collection ...  1.0  1.0   \n",
      "1  Cataloging,Processing,Electronic Resources,Acq...  2.0  2.0   \n",
      "2                                         Cataloging  1.0  1.0   \n",
      "3  Cataloging,Processing,Electronic Resources,Int...  1.0  NaN   \n",
      "4  Cataloging,Processing,Acquisitions,Collection ...  2.0  2.0   \n",
      "\n",
      "                                                  Q8   Q9             Q10  \\\n",
      "0                                              Other   No             Yes   \n",
      "1  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "2  We did not temporarily or permanently lose sta...   No              No   \n",
      "3  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "4  We did not temporarily or permanently lose sta...   No              No   \n",
      "\n",
      "   ...                                                Q29  Q30  \\\n",
      "0  ...                                                NaN  NaN   \n",
      "1  ...                                                NaN  NaN   \n",
      "2  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "3  ...       Change to Electronic Resources and/or eBooks  NaN   \n",
      "4  ...   Change to work environment (e.g. work-from-home)  NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0                                     Not applicable   \n",
      "1                                     Not applicable   \n",
      "2  I neither agree nor disagree about the success...   \n",
      "3  I somewhat agree that these changes have been ...   \n",
      "4  I strongly agree that these changes have been ...   \n",
      "\n",
      "                                                 Q32                 Q33  Q34  \\\n",
      "0                                     Not applicable      Not applicable  NaN   \n",
      "1                                     Not applicable      Not applicable  NaN   \n",
      "2  I neither agree nor disagree on the impact tha...   Somewhat negative  NaN   \n",
      "3  I somewhat agree that these changes will have ...   Somewhat positive  NaN   \n",
      "4  I strongly agree that these changes will have ...  Extremely positive  NaN   \n",
      "\n",
      "   Q35        Q36                  Q37  \\\n",
      "0  Yes  Oklahoma   K-12 School Library   \n",
      "1  Yes   Oklahoma  K-12 School Library   \n",
      "2  Yes   Oklahoma  K-12 School Library   \n",
      "3  Yes   Oklahoma  K-12 School Library   \n",
      "4  Yes   Oklahoma  K-12 School Library   \n",
      "\n",
      "                                              Q38  \n",
      "0                                       Librarian  \n",
      "1                                       Librarian  \n",
      "2                                       Librarian  \n",
      "3                                       Librarian  \n",
      "4  Library Assistant, Associate, Paraprofessional  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "           Q1   Q2                    Q3   Q4  \\\n",
      "40        Yes   No  Three Months or less   No   \n",
      "41  As-Needed   No  Three Months or less   No   \n",
      "42        Yes  Yes        1 Year or more  NaN   \n",
      "43        Yes  Yes  Three Months or less   No   \n",
      "44  As-Needed   No  Three Months or less   No   \n",
      "\n",
      "                                                   Q5   Q6   Q7  \\\n",
      "40  Cataloging,Processing,Electronic Resources,Int...  1.0  1.0   \n",
      "41  Electronic Resources,Acquisitions,Library Syst...  1.0  1.0   \n",
      "42  Cataloging,Electronic Resources,Interlibrary L...  1.0  1.0   \n",
      "43                                              Other  NaN  NaN   \n",
      "44  Cataloging,Processing,Electronic Resources,Acq...  2.0  2.0   \n",
      "\n",
      "                                                   Q8   Q9           Q10  ...  \\\n",
      "40  We did not temporarily or permanently lose sta...  NaN            No  ...   \n",
      "41                                     Not applicable  NaN            No  ...   \n",
      "42  We did not temporarily or permanently lose sta...  NaN            No  ...   \n",
      "43                               Voluntary Retirement  Yes  I'm not sure  ...   \n",
      "44  We did not temporarily or permanently lose sta...  NaN            No  ...   \n",
      "\n",
      "    Q29  Q30                                                Q31  \\\n",
      "40  NaN  NaN                                     Not applicable   \n",
      "41  NaN  NaN                                     Not applicable   \n",
      "42  NaN  NaN                                     Not applicable   \n",
      "43  NaN  NaN  I somewhat agree that these changes have been ...   \n",
      "44  NaN  NaN                                     Not applicable   \n",
      "\n",
      "                                                  Q32                Q33  Q34  \\\n",
      "40                                     Not applicable     Not applicable  NaN   \n",
      "41                                     Not applicable     Not applicable  NaN   \n",
      "42                                     Not applicable     Not applicable  NaN   \n",
      "43  I somewhat agree that these changes will have ...  Somewhat positive  NaN   \n",
      "44                                     Not applicable     Not applicable  NaN   \n",
      "\n",
      "    Q35         Q36                  Q37                             Q38  \n",
      "40  Yes         NaN  K-12 School Library                       Librarian  \n",
      "41  Yes    Oklahoma  K-12 School Library                Dean or Director  \n",
      "42   No  Washington  K-12 School Library  Manager, Supervisor, Unit Head  \n",
      "43   No    Oklahoma  K-12 School Library                           Other  \n",
      "44  Yes    Oklahoma  K-12 School Library                       Librarian  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#K-12\n",
    "\n",
    "#Import File\n",
    "k12=pd.read_csv('Impact_of_COVID_on_Tech_Services_K-12.csv')\n",
    "\n",
    "#Data Analysis\n",
    "print(k12.head()) #Column names, data samples\n",
    "print(k12.tail()) #Tail sample, 45 rows\n",
    "k12.describe() #5 rows with 38 columns\n",
    "type(k12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fc3dcc97",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      Q6   Q7\n",
       "0    1.0  1.0\n",
       "1    2.0  2.0\n",
       "2    1.0  1.0\n",
       "4    2.0  2.0\n",
       "5    5.0  5.0\n",
       "6   22.0  2.0\n",
       "7    1.0  1.0\n",
       "8    4.0  6.0\n",
       "9    1.0  1.0\n",
       "10   1.0  1.0\n",
       "11   1.0  1.0\n",
       "12   1.0  1.0\n",
       "13   2.0  2.0\n",
       "14   1.0  1.0\n",
       "15   2.0  2.0\n",
       "16   1.0  1.0\n",
       "17   1.0  1.0\n",
       "18   1.0  1.0\n",
       "19   1.0  1.0\n",
       "20   1.0  1.0\n",
       "21   1.0  1.0\n",
       "22   2.0  2.0\n",
       "23   1.0  1.0\n",
       "24   1.0  1.0\n",
       "25   1.0  1.0\n",
       "26   1.0  1.0\n",
       "27   1.0  1.0\n",
       "28   2.0  2.0\n",
       "29   3.0  3.0\n",
       "30   1.0  1.0\n",
       "31   3.0  2.0\n",
       "32   2.0  2.0\n",
       "33   2.0  2.0\n",
       "34   1.0  1.0\n",
       "35   1.0  1.0\n",
       "36   1.0  1.0\n",
       "37   1.0  1.0\n",
       "38   2.0  2.0\n",
       "39   1.0  1.0\n",
       "40   1.0  1.0\n",
       "41   1.0  1.0\n",
       "42   1.0  1.0\n",
       "44   2.0  2.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Staffing Numbers Before and After\n",
    "k12_staffing=k12[['Q6','Q7']]\n",
    "k12_staffing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "799d8fbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9545454545454546\n",
      "1.5348837209302326\n",
      "86.0\n",
      "66.0\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(k12_staffing['Q6'].mean())\n",
    "print(k12_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 1.95\n",
    "# After: 1.53\n",
    "# Percent Change: 21.54% Decrease\n",
    "\n",
    "#Sum\n",
    "print(k12_staffing['Q6'].sum())\n",
    "print(k12_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 86\n",
    "# After: 66\n",
    "# Difference: 20 positions lost\n",
    "# Percent Change: 23.3% Decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "017d0ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Index 6 as Extreme Outlier\n",
    "k12_staffing=k12_staffing.drop(index=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cc778fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4883720930232558\n",
      "1.5238095238095237\n",
      "64.0\n",
      "64.0\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(k12_staffing['Q6'].mean())\n",
    "print(k12_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 1.49\n",
    "# After: 1.52\n",
    "# Percent Change: 2.01% Increase\n",
    "\n",
    "#Sum\n",
    "print(k12_staffing['Q6'].sum())\n",
    "print(k12_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 64\n",
    "# After: 64\n",
    "# Difference: 0 positions lost\n",
    "# Percent Change: 0%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f0309fae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  0\n",
      "Cataloging                                       38\n",
      "Processing                                       37\n",
      "Acquisitions                                     32\n",
      "Collection Development                           31\n",
      "Electronic Resources                             31\n",
      "Library Systems (e.g. Discovery or ILS support)  22\n",
      "Interlibrary Loan                                20\n",
      "Archives and/or Special Collections               9\n",
      "Other                                             7\n"
     ]
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "k12['Q5'] = k12['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "k12_areas=k12['Q5'].str.split(',', expand=True).stack().value_counts().to_frame()\n",
    "print(k12_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9dec68d2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k12_df={'Function':['Cataloging', 'Processing',\n",
    "                    'Acquisitions', 'Collection Development',\n",
    "                    'Electronic Resources', 'Library Systems',\n",
    "                    'Interlibrary Loan'\n",
    "                    'Archives and/or Special Collections', \n",
    "                    'Other'], 'Number of Units Including Functions': [38, 37, 32, 31, 31, 22, 20, 9, 7]}\n",
    "\n",
    "x=k12_df['Function']\n",
    "y=k12_df['Number of Units Including Functions']\n",
    "labels=['Cataloging', 'Processing',\n",
    "        'Acquisitions', 'Collection Development',\n",
    "        'Electronic Resources', 'Library Systems',\n",
    "        'Interlibrary Loan',\n",
    "        'Archives and/or Special Collections', \n",
    "        'Other']\n",
    "\n",
    "plt.pie(y, labels=labels, autopct='%1.1f%%')\n",
    "plt.title('Technical Services Participation by K-12 School Libraries')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('K12_Services.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9847532b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Q1         Q2                    Q3   Q4  \\\n",
      "0  Yes        Yes        1 Year or more  Yes   \n",
      "1  Yes        Yes        1 Year or more  Yes   \n",
      "2  Yes        Yes        1 Year or more  Yes   \n",
      "3  Yes  As-Needed            7-9 Months   No   \n",
      "4  Yes         No  Three Months or less   No   \n",
      "\n",
      "                                                  Q5   Q6   Q7  \\\n",
      "0  Cataloging,Processing,Electronic Resources,Acq...  2.0  2.0   \n",
      "1  Cataloging,Collection Development,Archives and...  2.0  2.0   \n",
      "2  Cataloging,Processing,Electronic Resources,Int...  3.0  3.0   \n",
      "3                Archives and/or Special Collections  1.0  1.0   \n",
      "4  Cataloging,Processing,Acquisitions,Collection ...  4.0  4.0   \n",
      "\n",
      "                                                  Q8   Q9             Q10  \\\n",
      "0  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "1  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "2  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "3                                          Furloughs  Yes              No   \n",
      "4  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "\n",
      "   ...                                                Q29  Q30  \\\n",
      "0  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "1  ...                                                NaN  NaN   \n",
      "2  ...   Change to work environment (e.g. work-from-home)  NaN   \n",
      "3  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "4  ...                                                NaN  NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0  I strongly agree that these changes have been ...   \n",
      "1                                     Not applicable   \n",
      "2  I strongly agree that these changes have been ...   \n",
      "3  I somewhat agree that these changes have been ...   \n",
      "4                                     Not applicable   \n",
      "\n",
      "                                                 Q32                 Q33  Q34  \\\n",
      "0  I strongly agree that these changes will have ...  Extremely positive  NaN   \n",
      "1                                     Not applicable      Not applicable  NaN   \n",
      "2  I strongly agree that these changes will have ...  Extremely positive  NaN   \n",
      "3  I somewhat agree that these changes will have ...   Somewhat positive  NaN   \n",
      "4                                     Not applicable      Not applicable  NaN   \n",
      "\n",
      "   Q35       Q36                                                Q37  \\\n",
      "0   No      Ohio                 Special Collections and/or Archive   \n",
      "1   No      Utah  Academic Library,Special Collections and/or Ar...   \n",
      "2  Yes       NaN                 Special Collections and/or Archive   \n",
      "3   No  Oklahoma  Academic Library,Special Collections and/or Ar...   \n",
      "4   No  Maryland                 Special Collections and/or Archive   \n",
      "\n",
      "                              Q38  \n",
      "0  Manager, Supervisor, Unit Head  \n",
      "1                       Librarian  \n",
      "2                       Librarian  \n",
      "3                       Librarian  \n",
      "4  Manager, Supervisor, Unit Head  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "           Q1   Q2                    Q3            Q4  \\\n",
      "29        Yes  Yes            4-6 Months           Yes   \n",
      "30        Yes   No  Three Months or less            No   \n",
      "31        Yes   No            4-6 Months            No   \n",
      "32        Yes  Yes        1 Year or more  I'm Not Sure   \n",
      "33  As-Needed   No            4-6 Months           Yes   \n",
      "\n",
      "                                                   Q5    Q6    Q7  \\\n",
      "29  Cataloging,Processing,Archives and/or Special ...  13.0  13.0   \n",
      "30  Cataloging,Processing,Electronic Resources,Acq...   7.0   7.0   \n",
      "31     Cataloging,Archives and/or Special Collections   3.0   3.0   \n",
      "32  Cataloging,Processing,Acquisitions,Archives an...  19.0  19.0   \n",
      "33  Cataloging,Acquisitions,Collection Development...   2.0   2.0   \n",
      "\n",
      "                                                   Q8   Q9             Q10  \\\n",
      "29  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "30                                              Other  Yes              No   \n",
      "31  We did not temporarily or permanently lose sta...  NaN    I'm not sure   \n",
      "32  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "33  We did not temporarily or permanently lose sta...  Yes              No   \n",
      "\n",
      "    ...                                                Q29  Q30  \\\n",
      "29  ...                                                NaN  NaN   \n",
      "30  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "31  ...  Change to a workflow,Change in position descri...  NaN   \n",
      "32  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "33  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "\n",
      "                                                  Q31  \\\n",
      "29                                     Not applicable   \n",
      "30  I somewhat agree that these changes have been ...   \n",
      "31  I strongly agree that these changes have been ...   \n",
      "32  I somewhat agree that these changes have been ...   \n",
      "33  I strongly agree that these changes have been ...   \n",
      "\n",
      "                                                  Q32                 Q33  \\\n",
      "29                                     Not applicable      Not applicable   \n",
      "30  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "31  I strongly agree that these changes will have ...  Extremely positive   \n",
      "32  I somewhat disagree that these changes will ha...   Somewhat negative   \n",
      "33  I strongly agree that these changes will have ...  Extremely positive   \n",
      "\n",
      "                                                  Q34  Q35            Q36  \\\n",
      "29                                                NaN   No  Massachusetts   \n",
      "30                                                NaN   No        Indiana   \n",
      "31                                                NaN   No            NaN   \n",
      "32  extremely stressful work environment, too much...  NaN  Massachusetts   \n",
      "33                                                NaN  Yes       Oklahoma   \n",
      "\n",
      "                                                  Q37  \\\n",
      "29  Academic Library,Special Collections and/or Ar...   \n",
      "30                 Special Collections and/or Archive   \n",
      "31                 Special Collections and/or Archive   \n",
      "32                 Special Collections and/or Archive   \n",
      "33                 Special Collections and/or Archive   \n",
      "\n",
      "                               Q38  \n",
      "29  Manager, Supervisor, Unit Head  \n",
      "30  Manager, Supervisor, Unit Head  \n",
      "31                       Librarian  \n",
      "32                           Other  \n",
      "33                Dean or Director  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Special Collections And/Or Archives\n",
    "\n",
    "#Import File\n",
    "sca=pd.read_csv('Impact_of_COVID_on_Tech_Services_Special_Collections_Archives.csv')\n",
    "\n",
    "#Data Analysis\n",
    "print(sca.head()) #Column names, data samples\n",
    "print(sca.tail()) #Tail sample, 34 rows\n",
    "sca.describe() #5 rows with 38 columns\n",
    "type(sca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2bfafc78",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q6    Q7\n",
       "0    2.0   2.0\n",
       "1    2.0   2.0\n",
       "2    3.0   3.0\n",
       "3    1.0   1.0\n",
       "4    4.0   4.0\n",
       "5    2.0   2.0\n",
       "6    1.0   1.0\n",
       "7    1.0   1.0\n",
       "8    1.0   1.0\n",
       "9   11.0  11.0\n",
       "10   4.0   4.0\n",
       "11   1.0   1.0\n",
       "12   1.0   1.0\n",
       "13   3.0   3.0\n",
       "14   1.0   1.0\n",
       "15   3.0   2.0\n",
       "16   8.0  11.0\n",
       "17   4.0   4.0\n",
       "18   3.0   4.0\n",
       "19   1.0   1.0\n",
       "20   1.0   1.0\n",
       "21   4.0   0.0\n",
       "22   5.0   5.0\n",
       "23   6.0   6.0\n",
       "25   2.0   2.0\n",
       "26   4.0   4.0\n",
       "27  12.0   8.0\n",
       "28   1.0   1.0\n",
       "29  13.0  13.0\n",
       "30   7.0   7.0\n",
       "31   3.0   3.0\n",
       "32  19.0  19.0\n",
       "33   2.0   2.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Staffing Numbers Before and After\n",
    "sca_staffing=sca[['Q6','Q7']]\n",
    "sca_staffing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b55cee21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.121212121212121\n",
      "3.9696969696969697\n",
      "136.0\n",
      "131.0\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(sca_staffing['Q6'].mean())\n",
    "print(sca_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 3.67\n",
    "# After: 3.68\n",
    "# Percent Change: 0.27% Increase\n",
    "\n",
    "#Sum\n",
    "print(sca_staffing['Q6'].sum())\n",
    "print(sca_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 88\n",
    "# After: 92\n",
    "# Difference: 4 positions gained\n",
    "\n",
    "# Post Inclusion of Academic Libraries in Data Restructure\n",
    "# Before: 4.12\n",
    "# After: 3.97\n",
    "# Percent Change: 3.97% Decrease\n",
    "\n",
    "# Positions Lost\n",
    "# Before: 136.0\n",
    "# After: 131.0\n",
    "# Difference: 5 positions lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "67c7a7e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  0\n",
      "Cataloging                                       30\n",
      "Processing                                       24\n",
      "Archives and/or Special Collections              22\n",
      "Electronic Resources                             15\n",
      "Acquisitions                                     14\n",
      "Collection Development                           13\n",
      "Library Systems (e.g. Discovery or ILS support)  13\n",
      "Federal Depository Library Program                5\n",
      "Other                                             5\n",
      "Interlibrary Loan                                 5\n"
     ]
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "sca['Q5'] = sca['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "sca_areas=sca['Q5'].str.split(',', expand=True).stack().value_counts().to_frame()\n",
    "print(sca_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d3ed54fa",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qFGtsWCwWi8ViqVGssWGxWCwWi6VGscaGxWKxWCyWGsUaGxaLxWKxWGoUa2xYLBaLxWKpUayxYbFYLBaLpUaxxobFYrFYLJYaxRobFovFYrFYahRrbFgsFovFYqlRrLFhsVgsFoulRrHGhsVisVgslhrFGhsWSwlExCMia0Rkg4jMFJHoOqBTaxH5INh6WCwWS1WwS8xbLCUQkSxVjXW23wFWquozPvtDVNUTNAUtFovlDMN6NiyWsvkB6Cwio0Vkjoj8F1gvIpEi8oaIrBeR1SIyBowhIiL/cMrXicg9TvkAEZknIitF5GsRaeWU3ysim5y67zlloxzPyhpHdpyIJIrIBmf/7SLykYh8JSLbROTvRcqKyF0islVE5orIKyLyQm2fMIvFYilJaLAVsFjqKiISClwMfOUUDQZ6q+ouEXkAQFWTRKQ78I2IdAXuADoA56iqW0Qai0gY8DxwuaoeEZHrgCeAO4HJQAdVzReRhk4/DwKTVHWhiMQCeaWo1w84B8gHtojI84AHeBjoD2QCs4G1ATwlFovFUiWsZ8NiOZ0oEVkDrAB+BF5zypep6i5nezjwFoCqbgZ2A12B8cBUVXU7+44D3YDewLeO3IeABEfOOuAdEbkZcDtlC4FnROReoGGRrBJ8r6rpqpoHbALaY4yheap6XFULgZnVPhMWi8USAKxnw2I5nVxV7edbICIA2b5FftoKUDIQSoCNqjq0lPoTgJHAT4CHRaSXqk4RkVnAJcASERnP6d6NfJ9tD+a77E8ni8ViCSrWs2GxVI35wE0AzvBJO2AL8A0w0RmCQUQaO+XNRGSoUxYmIr1ExAW0VdU5wO+BhkCsiHRS1fWq+iTGu9K9gjotA0aJSCOn/6sCdKwWi8VSLayxYbFUjZeAEBFZD8wAblfVfOBVzNDLOhFZC9yoqgXA1cCTTtka4DwgBHjbkbEaeFZV04D7nWm3a4Fc4H8VUUhV9wF/BZYC32GGV9IDdLwWi8VSZezUV4vlLEJEYlU1y/FsfAy8rqofB1svi8VSv7GeDYvl7CLZCULdAOwCPgmqNhaLxYL1bFgsFl+S40OAlpjZMm1K+d8KM/xTABSW8b8QE8S6HxOzYl7J6Udq8WgsFksdwRobFkt9JDk+GhgInAsMABIxxkRLjDFRU5wAtuJrgJjXNpLT88tqaLFYzlyssWGx1AeS45sDYzHTbM8FkqhbU9+9mJwj3wHfA/NJTs8JrkoWiyVQWGPDYjkbSY6PwRgW451XEmdWHo4CYMljhTfPfN1zyQ+pUybYTKgWyxmMNTYslrOF5HgBRgF3AT8Fgr5abXU5L+9fB/bTtBVmOvFnwH9Tp0xYHGS1LBZLJbHGhsVyppMcnwDc7rw6BVWXAJKr4Vt75E/rWsquzcA04K3UKRP2165WFoulKlhjw2I5E0mODweuwCzmdj5n4TT27z3nzL2r8Hejy6jiwWRsnQZ8mjplgg0wtVjqKNbYsFjOJJLj+2CGSW4CmgRZmxrlzoIH18329u9TweqHgX8CL6VOmWCzplosdQxrbFgsZwLGyHgSuCjYqtQGqqR3zn8rxkNIZWfMpAMvA8+mTplwuAZUs1gsVcAaGxZLXSY5vj3wOMaTcdYNlfgj1dtiyeiCZ8+thog84HXgqdQpE1IDo5XFYqkq1tiwWOoiyfFNgD8BvwIigqxNrfNs4VULnvNcNTwAogqBF4BH7fCKxRI8rLFhsdQlTGbP+zFLzscHV5ngoIoOzX/h8EEatwig2CPAw8ArqVMmeAMo12KxVABrbFgsdQGzJsldwJ+B1kHWJqjkaMTmnvlvdK8h8euA+1OnTJhTQ/ItFksp1JsxYIulzpIcPwhYD/ybem5oACz09j5Ug+L7ALMTJ8/6IHHyrJY12I/FYvHBGhsWS7BIjneRHP9/wEKgR7DVqSu85RnfqBa6uQrYkDh51tW10JfFUu+xwygWSzBIjm8DvIlZHM3i4FXSOue/HefFVZMrz5bkv8Ck1CkT0mqxT4ulXmE9GxZLbZMcfwWwFmtonMYubZVSy4YGwI3A+sTJs86v5X4tlnpDXVpi2mI5u0mOjwKeBe4Otip1lY88IzxB6joB+Dpx8qzngQdTp0woDJIeFstZiR1GsVhqA5MB9F2gZ7BVqauoooPyXzp6lIbNgqzKPODq1CkTjgZZD4vlrMEOo1gsNU1y/L3AMqyhUSbZRKbUAUMDYBSwInHyrL7BVsRiOVuwxobFUlMkx4eQHP8K8Bz1MAtoZfnBm3Qk2Dr40B5YlDh51jXBVsRiORuwxobFUhOY+IyPgZ8FW5Uzhbc959e1VWyjgfcTJ8/6S+LkWRJsZSyWMxkbs2GxBBqzrsnnwNBgq3Km4FU53in/rYaKq64+AL0B/MymOrdYqkZd/WJbLGck573eq02uyPdYQ6NS7NBWKXXY0AC4A3gncfIsO4PPYqkCdfnLbbGcUSRNT0rMDHEt+ElCq3w3uIOtz5nEh56RZ8IwxfXAB4mTZ9n4G4ulklhjw2IJAEnTkzoB84HEg6Ghg69q02qpF6zLvQKo4n3fM6pbsPWoIJcDnyVOnhVVHSEi0lJE3hORHSKySUS+FJGufuo2FJFfVVBuVjV0elVE7IwpS41gjQ2LpZokTU/qhjE02haV7QwPG3ZrqxYLgqfVmUMWUSnHia9rwaFlcQHwVeLkWTFVaSwiggkenquqnVS1J/BHoIWfJg2BChkb1UFVf6aqm2q6H0v9xBobFks1SJqe1BaYQymrta6NjBg5qUWzebWv1ZnFPG/fMzF51kjMTJWqxHCMAQpVdWpRgaquAVaLyPciskpE1ovI5c7uKUAnEVkjIk+JSKyfeicRw1MissGpc51T7hKRl0Rko4h84XhUrnb2zRWRgc52log8ISJrRWSJiLRwyjs575eLyGPV8aRY6hfW2LBYqkjS9KRYzKyTVv7qzI+OGvVw08Zza02pM5C33OPrQiKvqnAJ8O8qtOsNrCylPA+4UlX7YwySpx0vyGRgh6r2U9XflVHPl58C/YC+wHjgKRFp5ZQnAkmYadn+ApljgCWq2hfjtfu5U/4c8JyqDgL2V/bALfUXa2xYLFUgaXqSC7NaaLlZJj+Jix39bKP4+TWv1ZmHV+XIUu3RI9h6VIM7EyfPeixAsgT4q4isA74D2lD60EpF6g0H3lVVj6oewqRgH+SUz1RVr6oexHjlSqMA+MLZXokxUMAYJzOd7f9W7vAs9RlrbFgsVeNJ4LKKVn49vsHwaQ3iFtWgPmck27TNVjjtqfxM4+HEybN+UYn6G4EBpZTfBDQDBqhqP+AQEFnFev7OaUXPdaGeSsLkwS7aaakm1tiwWCpJ0vSku4AHK9VIxPV044aDP4mNWVYzWp2ZzPSMOtMNjSJeSpw8q6LG52wgQkSKhiYQkUGYFOmHVbVQRMY47wEygTif9vF+6vkyH7hOREJEpBkmxmQZsAC4yondaAGMrvghArAEuMrZvr6SbS31GGtsWCyVIGl60mjg5So1Fgl9uGnjPnOjo9YEUqeS3PlpLs2fyqT3S8Vj955fWkC3F7Lo9VIWv/82z297j1c5599ZXPrfnJNlf/g2jz4vZ3Hrx7kny95aW8BzS/KrrKcqng88I8/kIRRfQoD/Jk6eVe7xOB6DK4HznamvG4Fk4EtgoIiswHgvNjv1jwELnWDPp4B3SqtXgo+BdcBajHHze2fY5ENgL7ABE2+yFEivxHHeD/xWRJZhYpUq09ZSj7Hpyi2WCpI0Pakz5se5cbUEqWa+cfDwnoF5+TWS02D+bjex4cKtH+ey4VexAMzZ5eaJH/KZdWM0EaHC4WwvzWNKf9Z4ZnE+K/Z7yMiHL26MJj1PufTdHH64I4abPsph8rAIOjd2cem7OXx1UzRhIVVzTmRo9IY++a/2rvKB1k1SgMGpUybU2VkaIhKrqlki0gTj7RjmGCIVaRsN5Kqqisj1wA2qetpsGIulJNazYbFUgKTpSQ0xAXPVMzQAROLubNm8ZUp42I5qyyqFke1DaRxV3AB4eUUBk4dHEBFqyv0ZGnszvMza5uZn/cNPlrkECjyKqpJbCGEh8NSiAu4dHF5lQwNgjrffmTjltTx6AK8GW4ly+EJE1gA/AI9X1NBwGACscYJTfwU8UAP6Wc5CrLFhsVSM14CAZblUkcbXt24Zszs0dE+gZJbF1mNeftjtZsirWYyals3yfZ5S693/VR5/Hx+Jy8eGiIsQruoRxjn/zqZDQxfxEcLy/R4u7x5WLZ3edo9vWS0BdZfrEifPujvYSvhDVUc702h7quq0Srb9QVX7qmofVR2pqttrSE3LWYY1NiyWckiannQtJj9BQPGKtLwioZUeCgk5FGjZJXF74UQeLLkrhqfOj+TaD3IoOYT6xdZCmscIA1qHnNb+98MiWDMxlqcvjOThOfk8NjqCV1cVcO3MHP4yv/JxGx6VI8u125mSorwqPJs4edbZNkRksVQZa2xYLGWQND2pKfB8Tcl3i7SbkNAqM83lOlFTfQAkNBB+2iMUEWFwmxBcAkdzihsbC3/08NkWN4n/zOT6D3KZvcvNzR/lFquz+oDxiHRt4uLNtYW8f000Gw572HasdE+JP7Zo27NhymtZRAEz7KJtFovBGhsWS9n8C2hekx3ku1ydL2rb+kCWSGZN9XFF9zBm7zIL0W495qHAA02ji9/r/zY+kr2/jSP1/jjeuzqKsR1Cefunxdcbe3hOPo+NiaDQCx7HVnEJ5BRWTp/3PaPrw29PT+ChYCthsdQF6sMX3mKpEknTk34C3FAbfWW7XD0vbtt6R77gf05qBbnhwxyGvpbNlmNeEp7J5LVVBdx5Thg7Tyi9X8ri+g9ymX5FFCLC/kwvl7yTU75Q4JPNhQxqHULrOBcNI4WhCSEkvZyFCPRtefrQiz9U8XzkGV5fVhf9Q+LkWb2CrYTFEmzs1FeLpRSc2ScbKWWBtZqkpdu97H979vcPPYszNqZpzLp++a/0CbYetcgiYHjqlAn2x9ZSb7GeDYuldJ6hlg0NgIOhoYOvatNqqRe8td13bfG9t//xYOtQy5wHTAy2EhZLMLHGhsVSgqTpSRcAdwSr/53hYcNubdViQbD6r2nedo+vdSOuDvC3xMmz6uNxWyyANTYslmIkTU+KAV4Jth5rIyNGTmrRbF6w9Qg0HpWDq7VL12DrEQTigX8GWwmLJVhYY8NiKc4koF2wlQCYHx016uGmjecGW49AkqLt63MSqGsSJ88aFGwlLJZgYI0Ni8XB8WpUbjXXGuaTuNjRzzaKnx9sPQLFDM/oszbwtYL8NdgKWCzBwBobFsspfgU0C7YSJXk9vsHwaQ3iFgVbj+qiivvj+jPl1R/jEyfPGhtsJSyW2sYaGxYLkDQ9KRr4XbD1KBUR19ONGw7+JDZmWbBVqQ5pxG7IIrpBsPWoA1jvhqXeYY0Ni8UwiTro1TiJSOjDTRv3mRsdtSbYqlSVbz0D0oOtQx1hSOLkWXZZdku9whoblnqP49WoU7EapSISeU/zpp1WREZsCrYqVeFtT72c8uqPvyROnnU2rw1jsRTDGhsWi4nVqNH1TwKGSNydLZu3TAkP2xFsVSqDW10H1mmnLsHWow7RG7go2EpYLLVFhYwNEblSRFREule2AxFJFZGmpZT/REQmV1ZebSAiiSKyoUTZShEJr6K8c0VkqYisEZEUEUkOiKKn5E8UkVvLqZMsIqU+vYvIrSKyQUQ2isgmf/V86k8Tkaud7bkiMrAKOvcTkUt83gfleqjTsRp+UJHG17duGbM7NHRPsHWpKBs1sT5PefXHPcFWwGKpLSo6De0GYAFwPZBccqeIhKhqpdaYVtXPgM8q0yZYiEgisE9VCypYv+T5mA5cq6prRSQE6BZI/VR1alXbisjFwP3ABaq6X0QigVsCpVsZ9AMGAl9CUK+HX3KmeDV88Iq0vCKh1Y9f7dl/qIXH0yLY+pTHe54xVTLUfXFnHOHorGfwZJ1AxEVsvwtpMPByPLmZHP30SdwZhwht0IKmV0wmJDL2tPZ7X74TV3gUuFyIK4RWt/0TgBNz3yB350rCm3eg6aUPAJC1YTbevEwaDKzR0IqLEifP6pI6ZcK2muzEYqkLlOvZEJFYYBhwF8bYKCofLSJzROS/wHoRCRGRf4jIehFZJyK+Vvs9IrLK2dfdaX+7iLwgIvGO98PllEeLyB4RCRORTiLyleNV+MGn7TXOk/haETktB4GIxIrI9z59Xu6UJzqehVecp/hvRCTK2TfAkbcYEyzoy8XAV069GxyZG0TkSZ8+s0TkMRFZCgwt0b45cABAVT2quslpkywib4nIbBHZJiI/95H3OxFZ7pzLR33Kb3XK1orIWz5yHnS2f+60WysiH4pIdJkfMPwf8KCq7nf0y1PVVxxZ/URkidPfxyLSqCxBInKBiCx2zvtM59pBRAaJyCJHp2UiEg88BlzneHuuK7oenPrtnc9vnfO/nVM+TUT+5cja6eNdaSUi8x1ZG0RkRDnHDEDS9CQBfl2RunURt0i7CQmtMtNcrhPB1qUsVCn4zHNe9ae8ukJoNOYu2vx8Ki1v+QeZq2ZRcPRHMpbMJDKxL21+8QqRiX3JWDLTr4gWN/yV1nc8f9LQ8OZnk78vhdZ3voCql4IjqXgL88ne8B1x50yotsrlIJz+W2OxnJVUZBjlCuArVd0KHBeR/j77BgN/UtWewC+ADsA5qtoHeMen3lFV7Q+8TIlAPFVNB9YCo5yiy4CvVbUQ+A9wj6oOcNq95NR5BLhQVfsCPylF5zzgSqfPMcDTIlIUjNUFeFFVewFpwFVO+RvAvapa0lAAM7b6lYi0Bp4ExmKezAeJyBVOnRhgg6oOUdWS61o8C2xxbth3O96DIvoAEzAGyiMi0lpELnD0HOz0M0BERopIL+BPwFjn2O8rRdePVHWQsz8FYySWRW9gpZ99bwJ/cD7P9cCf/QkRM1T2EDDeOe8rgN+KGXqaAdzn6DQeyMZ8hjNUtZ+qzigh7gXgTZ/r6F8++1oBw4FLgSlO2Y2Ya6Yf0BdYU84xFzEWSKxg3TpJvsvV+aK2rQ9kiWQGWxd/HCduYzZRcdWVExrbmIiWnQFwRUQT1qQtnsxj5GxfSkzvcQDE9B5HzrYllZAqqMeNqqLuAsQVQsayj4gb8BMkpFbyj92ROHnW6W4Yi+UsoyLGxg3Ae872e877Ipap6i5nezwwVVXdAKrqu7LjR87/lZT+4z4DuM7Zvh6Y4TwVnwfMFJE1wL8xNxqAhcA0xxMQUoo8Af4qIuuA74A2QJGreZeqrvHVx3nSbqiqRWtRvHVSkLlZJqjqTmAQMFdVjzjH+Q4w0qnqAT4sRRdU9THMkME3mBvjVz67P1XVXFU9CszBGBgXOK/VwCqgO8b4GAt84NQteY6L6O14gdYDNwG9StOpPEo5J9N9jrU0zgV6Agudz+s2oD1myOiAqi53dM4oukbKYCjwX2f7LYxxUcQnqup1vENFn+ly4A4xsTBJqlrRG2/QFlsLJNkuV8+L27bekS/kBVuX0vjaMygj0DLd6YcoOLSTiNbd8GSnERrbGDAGiTc7rfRGIhx+/xEOTLuPzDXmK+iKiCa623kcmHYvofEtkIgYCg5sJbrLuYFW2R8NMN8Vi+WspkzTXUSaYG5wvUVEMTd2FZHfO1WyfasD6kdUvvPf46fPz4C/iUhjYAAwG+MpSHOeVouhqhNFZAjGI7BGRPqp6jGfKjdhciYMUNVCEUkFirwJ+T71PEBUObqPwMSrFB2jP/LKiltR1R3AyyLyCnDEObeU0q86/fxNVf/tu0NE7i1DzyKmAVc48SG3A6PLqb+RU+e8OgjwrareUKxQpA/l61wevu19Pz8BUNX5IjIScz28JSJPqeqbZQlMmp4UD/y0mnrVGdJCQvpdmtB62f/27O8fWvFYrFrhbc/4hEDK8xbkcuTjv9J43M9xRZQ3SniKljf9ndC4Jniy0zg04yHCmiQQ2bY38UOuJn7I1QAc+9+/aDjiZjLXfk3ertWENU+k4XnXlyO52vwceLGmO7FYgkl5no2rMe7s9qqaqKptgV0Uf9Is4htgooiEAjiGQ4VQ1SxgGfAc8IUT15AB7BKRaxx5IiJ9ne1OqrpUVR8BjgJtS4iMBw47hsYYzBN2Wf2nAekiUnRcN/nsvgj4n7O9FBglIk3FBHreAJS7MqeITCgxjOPBDOEAXC4ikY7xMRrzlP41cKdPzEMbEWkOfA9cW2So+DnHccABEQkrcRz++BvwdxFp6ciMEJF7neGtEz7xD7eUc6xLgGEi0tmREy0iXYHNQGsRGeSUxznXSKaja2ks4lR80E2cMvZKRUTaYz7vV4DXgP5l1Xe4HmNonjUcDA0dfFWbVku94A22LkW41bV3kyZ2CpQ89bg58vFfiek5muhu5wEQEtMQd5Zx8rmzjuOKaVhq29C4JifrR3cdSv7+rcX2Fxwys4lDG7Uhe8Nsml0xmcIjuyk8vi9Q6vujb+LkWfVxJVxLPaI8Y+MG4OMSZR9ihgJK8irwI7BORNb6qVMWM4Cbnf9F3ATc5cjbCBSFhj8lTpAmMB8T8+HLO8BAEVnhyNhcgf7vAF4UEyCa61M+Gucmq6oHMAGVc5w+V6nqpxWQfQsmZmMNZljgJh8vyDJgFuZm/biq7lfVbzDDCIud4ZAPgDhV3Qg8AcxzzskzpfT1MMYo+rYix62qX2Keqr4TkY2YoaWiJ+PbMOd6HSZ25LEy5BwBbgfedeovAbo7M3iuA553dP4W42WaA/QsChAtIe5ezLDIOsy5Ky02xZfRGA/XakwMznPlHTfFhwPPGnaGhw27tVWLMo2z2mSddtwZKFmqyrH/PUdYk7Y0GHzlyfLozkPI3vA9ANkbvie685DT2noL8vDm55zcztu1mvBmxZ9B0n54m/jhN4HXDerYa+JC3fklxdUE19ZGJxZLsBDV6nq4z15EJAF4RVUvriH5yUCWqv6jJuRbSidpelJLYB9ncVK7kTm58148dGRU+TVrlt8X/nzZ+54xgwMhK2/vRg698wfCmiWC4yhsNPJWwlt34+inU3BnHCG0QTOaXv5/hETF4c48xrGv/kWLax6lMO0gRz76ixHk9RLTcxTx552ycXO2Lqbg8C4aDjfPSCdmv0burlWENU+k2WW1koZlfeqUCX1qoyOLJRhYYyOIWGMjOCRNT/o18Hyw9ahprsjMmvv40eOjg9W/Kvm98l935xAZEywdzjB6pE6ZUBEvrMVyxnHWPtmdCahqsjU0gkK9cFl/Ehc7+tlG8afloaktjhK/wRoaleKaYCtgsdQU1tiw1CucIZRhwdajtng9vsHwaQ3iFgWj7688g7KC0e8ZjDU2LGct1tiw1DdGUp+uexHX040bDv4kNmZZbXf9tmd8u9ru8wwnKXHyrFblV7NYzjzqz4+uxWKoN16Nk4iEPty0cZ+50VFraqvLQg35cYu261Bb/Z1FBD2o12KpCepU8h+LpRYIuLGx97W9ZK7JJLRBKF2eMKuoH/r4ECfmnSA0znzFWlzdgri+p6cVKa0twMH3D5K5LpOodlEk/MLkxDqx8ASebA9NLzhtEeXyEYm8p3nTTm8cPLxpYF5+9dcpKYc12ikVsJ6NyjOKUxmbLZazBuvZsNQbkqYnxWDWTgkojYY3IvGBxNPKm17YlM6Pd6bz451LNTT8tfXkeMjZnkOXv3RBvUrenjy8BV7SFqTRZGyTUuVUCJG4O1s2b5kSHraj6kIqxrvusWdVwrRaZHSwFbBYagJrbFjqE4OpAW9eTLcYQmJKW6Knim0F1K1mcbBCRUKEo/87SpPzmyChZWXMLx8VaXx965Yxu0ND91RLUFl9KHlfeof0rin5ZzndEyfPalF+NYvlzMIaG5b6RK3Gaxz77hjbHtrG3tf24sn2u2zOaYREhdBgYAN2PLKDsKZhuKJd5O7MpUH/BgHRyyvS8oqEVnooJORQQASW4DANN+YRYT0bVaesBQ8tljMSG7NhqU/UmrHRZGwTml/eHIDDHx3mwHsHSLir4uuRNbukGc0uaQbAvtf30fynzTk+7zhZG7KIbBtJ8580r5Z+bpF2ExJabf9uz/7whl5vo2oJK8GXniF2ymv1GAnMDLYSFksgsZ4NS70gaXqSC7N0fa0QGh+KuARxCY1GNSJ3Z275jUohd7dpF9EygrSFabSb1I78vfnkH6z+eh35Llfni9q2PpAlklltYT684xmXGEh59RCbttxy1mGNDUt9oRdmNeBaoTCt8OR2xqoMIttEVknO4Y8O0/zK5qhbT63l6gJvQWAWds12uXpe3Lb1jnwhLxDyCjRk93ZNKHOVZUu51PhsIYultrHGhqW+cG5NCd7z8h52/mUn+Qfz2fybzRyfd5yDMw6y7aFtbHtoG9kp2bS8sSUAhScKSX0mtcy2RWSszCCqQxRhjcIIiQkhqnMU2x7aBkBUu8CFRKSFhPS7NKH1Oje4qytrlXZNDYBK9Z2miZNnNQu2EhZLILELsVnqBUnTk6YAfwi2HnWZjgWFCz/ed2CoqxoPIfcWTFr5mXfYgEDqVU8ZlTplQtDWtbFYAo31bFjqCxWPzqyn7AwPG3ZrqxYLqtpeldyvvYN6BVKneowdSrGcVVhjw1JfaBNsBc4E1kZGjJzUotm8qrQ9SKMN+YRXLTjFUhJrbFjOKqyxYakvWM9GBZkfHTXq4aaN51a23SzP0KpNubGURvdgK2CxBBJrbFjqC9azUQk+iYsd/Wyj+ErFDLztGWcXXgscNouo5azCGhuWs56k6UmNAZvRspK8Ht9g+LQGcYsqUjdfQ3elaqu2Na1TPcLORrGcVVhjw1IfsEMoVUHE9XTjhoM/iY1ZVl7Vld6uP9aGSvWIKizta7HUXayxYakP2CGUqiIS+nDTxn3mRketKavaO57xsbWkUX0hLHHyrIbBVsJiCRTW2LDUB6xnozqIRN7TvGmnFZERm0rbrUr2t94BdpXXwGOHUixnDdbYsNQHrGejuojE3dmyecuU8LAdJXftp8nGAsIigqHWWY4dSrGcNVhjw1IfsLkfAoCKNL6+dcuY3aGhe3zLP/cMDci6KpbTaBJsBSyWQGGNDUt9IDCrllnwirS8IqGVHgoJOVRU9l/PuI7B1OksJjzYClgsgcIaG5b6gCfYCpxNuEXaTUholZnmcp3I07AdP2oLGxNTM4QGWwGLJVBYY8NSH7DGRoDJd7k6X9S29YEvSdoVbF3OYkKCrYDFEiis5WypD1hjoxqEuTWv9TH2tz+sxxIPaW7bI2jLExrpDmtbcHDIhUevHLjuw+1NG8Zuc3VtkCaN2oHYBGqBwKP2urWcNVhjw1IfsDEbZeDyqqdZOgfaHtGjiYfIan9Y3a2Pa3jjTOKiCmguSnOBjpgXAJmxCTuW9/t9AznwerNfr/lZ868bzF9zc+f3Cxo23Ru2Q7oeXsHgw+voF3KQ1m3cEpYYvKM7gwkVDbYKFkugsMaGpT5Q758Q47P1aMJRPdT+EOkdDmlhm2PqappOTGweTUK8tBaTi6RCsRcZce23ruj/YGPVHG+eJ6O725Oz7CeZ48YuWd923tKwXWGJHVYfvqH1Wx1vcr2ZAJChccfXcs7OlQzJ3kq3uHQadkIkvkYP2EEL8jl+311QWIB6PESOGk/s7b8stW7h5o0c//WtxD88hchR5+NNO07aIw/gzcok9s5JRA4fA0DaQ/cTd/8fCWnavKbVL6zpDiyW2sIaG5b6wFlvbEQUaHabY+xvd1hPdDikee2OoM3TNKpBDo3C3bQSk7Oh2nkb0hp02LzqnAeaI9LYW/DjCqD5phOLGNTsYs51dx3V3tNs05c7wjrs3DmgVZs2mxe3T1zbsEFIZo8RzG88ArOum4Lu1sQdKxl8YA39dS/tWhQQ3gmRwMcohIXT6Jn/4IqKRt2FHL/3TsIHDyO8Z59i1dTjIfM/zxE+cOjJsrzZXxF5wWVEjr2QtD8YYyN/0TxCu/SoDUMDwF0bnVgstYE1Niz1gTN+GMXlVXeLE+xvd0SPdjik2e0O42l1XMMbZREfWUALlzEkutSkDifiO29a3e/+1og0BPAUpmYB7Mpa339g0wuPiLiatdJGPW/OH5H+cfiyFfv29Ry6b19PmjTdvapz52UaHp43AEBAEkntlEhqp6t4H4BcIrM2ap/tKxicvoneUcdp0kHFVe0MmiKCREWbN243uN2IyGn1cj5+j8iR4yjcsvFUYUgoFORBYQG4XKjHTc6H/6XhE/+srloVxXo2LGcN1tiw1AfOCM9Go0w9nHBUD3U4RGbiIS1ofUxDmmYQG5NPc5eXlgLtMK9a53jDbhvW9L2nre/wh3r2RwEoGnowN3VTq+iOowAiCIu/vmDYkIWhW+alhOw979jR9v2PHW1PXNyRLV26Lj4aHZ0+RKT4b08UebEDWdZvIKfWfDuorfauZOCPqxnoTqVjk1yiuiBS6dwT6vFwfOKNePbtIeqK6wjrkVRsv+fIYfIXzKbR0/8pZmxEjruY9Cf+SO43XxD7i/vI/fR9Ii+YgETWWvzr8drqyGKpaayxYakPZAZbAYCoPM1IOMbB9of0eOJhzW97RGmeTnRcDo3DPLQWaI551SmONeqxbm2fSR0QifMtV2/myRiPdSfmJbSKLp7ba5i726gO3mYb/xe2Jl5FEzIzm3VbtfIn3SIjM/d16bJke3zDgwNE8LuAW0sOJEzg84QJfA5AIaH5m7XnhhUMObaBPuGHadHOKyHlpqKXkBCavDIDb1YmaY/8Fveu7YR26Hxyf+aLTxH7i/uQkOKjOK7YOBr97XkAvJkZ5Lw7jfjHnibjH4/hzcog+ppbCO/Vt7zuq8Oh8quAiGSpapkL4YnI/cB/VDWnMgqIyDTgC1X9QEReBZ5R1U0V6TPQiMho4EFVvbQ2+7UEBmtsWOoD+2qjk1CPFrQ8zv72h/Vo4mHNaXcYb8sTGtkwi/jIQloKNAIa1IYugeJIk6Q163vf3QWRGN9y9eYcBz15o08rONypwJu3IdwVWWxBttbexr1uyh+R9nHEsmXZkjcYIC8vrs369ee3CQ3NT+vYafm85s139RQpf9GxMNwRSazrncS6k2XHtfGh1QxIXcngvB10aZhFbBdEoktr74qNI7zvQPKXLSpmbBRu3UT645PNcaWnkb90AYSEngwIBch+8z/E3HwXed9/RWjXHkSOu5i0h39D42deKU/t6lAhY6OC3A+8DVTY2JASMTSq+rPKdCgioapabtxJRetZzmyssWGpD+wNiBRVbZrBwbZH9HDiITLbH1Z3m2Ma1jiT2Oh8mruUFgKJmNcZz+Gm/VZt6PWz7qXdvL3uPalAY9+y7RmrTvRseN5pciIJa3hD/rDBC0JT5m0O2T8MZwjF7Y5ouHXL8FHbtg7Nb9d+3Q8JCZsSXC5vh8ro2JjjLcbxbYtxfGv0wuXZrl22OFNvXXuPRyZ4wqPbueIaiObnUbBqKTHX315MRrP/zjq5nf7kI0ScO6KYoeHeuxvPsSOE9x2Ie/tWiIgAESjIr4yqlSXt4Jh+lerAefJPBo4CvYGVwM3APUBrYI6IHFXVMSJyAfAoEAHsAO5Q1SwRSQVeBy4AXighfy7Gs7DCef80MAY4AVyvqkecOouAYcBnIrIVeAiTev0YcJOqHhKRZEenROCoiLQF7lHVNY7shcAvVfWUZen/uG8A/ggIMEtV/+CUvwwMAqKAD1T1z055KjAduAwIA65R1c3l9WOpHtbYsNQHKmxsxOboCWeoI63DIS1IOKrSLJ3o2DyahJqhjlaY11nNoeYDVmzscUdvREpdxM5bmJpRsiwlbWm/HvFDc8SPZ2G4u8eoRG/z9V+HrWmiQuuictWQiN2p54zYndpPW7bctqxDx1VRoaGFSaXJKA8X3pCubOnWlS3dbuQtdp7I54kpxzyZ+eEFed4wb+zwsenh546IyflsZjxA9E+uKVdm1msvEnvXJAAix15E2sO/IefD/xJ7R+lTaAPEwSq2OwfoBewHFgLDVPVfIvJbYIyqHhWRphgDYLyqZovIH4DfAo85MvJUdTiAiFzkp58YYJWqPiAijwB/Bn7t7GuoqqOc9o2Ac1VVReRnwO+BB5x6A4DhqporIrcBtwP3i0hXIKKChkZr4ElH1gngGxG5QlU/Af6kqscdD833ItLHR+ZRVe0vIr8CHgQq5bWxVB5rbFjOetbftj4taXpSFhAbXqi5rcxQx/HEQ5rb7gja4oRGxOfQKOLUUEejYOscTA60GLI8pfstfRDxu2y8173vtH1uLYhLLziyoGFE8+H+2iV4myTdlD/ixMcRy5ZnS/6g4ntFDh7sOvjgwa40arRvfecuS3MiIrIHi3D69JEK0rFTBK+90joE83QLrIxRrtY9l7XftZJB+1frVu8e2rUoIKITIiHxf3jsNBkN//z3k9uuRo1p/ML0qqpTGVKr2G6Zqu4FEJE1GM/BghJ1zgV6AgudmTnhwGKf/TMq0I/Xp97bwEd+2icAM0SkldOPb3r7z1Q119meCTwsIr8D7gSmVUAHMJ6Luap6BEBE3gFGAp8A14rILzD3uVaYYy4yNor0XQn8tIJ9WaqBNTYs9YKpz7vnN8xmoCjNBDphXpYS7Gs1bOmWrjecU96sD/VmlBqYue7E/AYjW15dZh+RhDe6Pn/YwB9CU+ZuDTkwHDn9d+jEiTZJy5f9lJiYEzu7dF20Lzb2+GAR/Bo/lUFA2rG7Qzt2d7iSDwDIIyLbmXqbtoneUcdoGpCpt9UgtYrtfIdePJT+Gy/At6p6gx8Z2VXo1zfbqW/75zFBpZ/5DPOcVk9Vc0TkW+By4FpgYAX7LdUQFZEOGI/FIFU94QS6+nrpis6Tv3NkCTB2ITZLvaBxFm6XSbtd5afks529bUYu3tL1hgHlGxp56eBtW9q+A7k7+njUk1peX4LISHfP0RcW9tskygF/9bKzG3Vcs3rCiGVLr0o/dqzNXFXSyz0QoKDAy6Rf7eMXP9/LXXfuYfo0/7NIN2/O4yfnp8Rkz5vT925eHPVo2i8GN7nvvGau285z9/v691t66Ib5UZqz8cSf7vN6jh6uSPeBIDXA8jKBotlES4BhItIZQESinaGLyuACiqzKGznde1JEPKcCtG8rR+arwL+A5apa0Wm/S4FRItLUGS65AZiHCcTOBtJFpAVwcQXlWWoIa9FZ6gvbgq1AXebHhHGLtne6ckhFsnh63XtTAb9zPvdkpexOjOudWJF+23qb9Lkxf8SxjyOWrsiRAr9PswUF0c03bRzbPCSkMCuxw6p5rVpt6yKirf3VDwsT/vF0K6KiXLjdyv337WfQ4Dx69iweguLxKK++cpyBA0/lzpgzO4sLLohjzJjY0P+bPL/bcxfs6LZ4UTYpnQvdg5u8lLJChxzbQJ+wQ7Ro55XQcqfeVpHUAMv7D/A/ETngBIjeDrwrp4bKHgK2VkJeNtBLRFYC6cB1fuolAzNFZB/GyPEbAKyqK0UkA3ijjH7HiYhvDNY1wP8BczAPEl+q6qcAIrIa2AjsxMSvWIKIqNq1fixnPynde9wNTA22HnWR1HYXLNzZ4SfnVjRdeGH2d/M8BetG+dsfHRJ34NK2v2xecupkWSiq88I2zdvuOjgCqcjS6l53QttNS9u1W9c8JMRTZubUvDwv99+3n/vub0qPHsWNjQ8/TCc0BLZsyefcc6MZOSqWzz7LwONWxo6L5c+PHOLpZ1ox+Q8HePwvLYmMLO4MPkGjw2sYsGslg/K20zU+k7jTpglXkb4Hx/QrN0DybMIJ9pwLdFfVMz7rr6U41rNhqS+sDrYCdZGdiZf8kNr+kmGIVHhI1eveG1bW/hxPZqtcT9aK6NC4io67I4iMLuw1uqOrxdpvw9a2UKFl2S1coXv39B62d09vmjXbtbJjp+Wu8PD8c3xreDzKr365j337Crn88ganGRpHj7hZuCCbp/7Rii1bjpwsHzs2lr/+9RDffpvJz37ehM8+zeD88+NOMzQAGnGi+Ri+az6G7wAz9Xandtq6gsGH1nKO7KdNazdhHUrNke6fPGBTJeqf8YjIrcATwG+toXF2Yj0blnpBSvce4UAGBCbI8Gxge8fL5//Y9vwRlbwRknfiuZ3g6VhWnc5x/ZcMaHr+uVXRK4f8Ix9HLPsxVwoGVKZdgwaHUrp0XXIiKipjiPh4R7KyPPz5kUP8+p6mdOhwKhzlsUcPcfU18fTsGcnfnzx80rPhS2amh788fpjkR1vw0kvHyMr0cs018fTsVeqM4FLJJiZ9LedsX8HgrC30iEmjUeei9WX8sPTgmH5VOncWS13FGhuWekNK9x5LgcHB1qMusK3TVfP2JIwZWVlDQ7UgOz/thSjKCS534Sq4OvHBTBFpUhX9FPXOCds4f6fr0EikcoHskVEZe7p2XbyzQYPDg0SIBnjzzRNERgrXXtvwZL2bb/qRop+/9HQPkZHCb37TjGHDT42CvPTSUYYNi2Hv3kK8XuP1eOThgzz9jN9wkQocG7qXdqkrGbR/NQO8e2jfPJ+Izj7DWC8dHNNvUpU7sFjqIHYYxVKfsMYGsKXLdfP2tRnpN+aiLLzu/TuBchNuefGGH877cWOLqPYjq9KPIK6xhb1Hd3Q1X/192Po2KhVfMyYvt0HbxYuGtw0PLzyRlLRuWWzcrt6rVuY2vf76+GL13n7n1Jp2RZ4NX0Nj795Cjh3z0LdvFDu2FxAeISZxaEH1HtAEpC0/dmjLjx2u4EOjMxHZmzRp+3KGpB2l2QLoV60+LJa6hjU2LPWJZeVXqTp/OnCAedlZNA4J4bMOZpQhzePhgf372FdYSJuwMJ5p3Yb4Egt+5Xu93LrnRwpUcatyQVwc9zQ1KR6ePnKYH7Ky6R4ZwZRW5mn6s/R00r0ebmnUmMqS0u2muQdanTe6qsfoLdxV4ZVI1x2f1/L8NrdWtSsAEr3Nz7khf/iRjyKWrs6TwnPKb2HIysrik08+aeT1ekerevWcc1rtHjwkhM8/T2sPcNll5S9R8/rrx7nzTnOOx4yN5c+PHOTjj9K57fbA53yLJD+mPyv69mcFwC/gloD3YbEEEzuMYqk3pHTv0YXKTe+rFCtycoh2uZh8YP9JY+Mfhw8THxLCz5s04ZVjx8jwenigWfGHdFUlR5UYl4tCVW7+cTd/bN6CjuHh/HLfXt5u157f7d/Pz5s0pl2YKftPQlvCKjcCwsYet8891GLQ6OocY37GmwvVc3RYRev/tP1vUsJc4T2q0yeYYZXZYRvm73IdrvSwio8Ub6vWW5YmJq6ODw1196yuTjXE4XFjd7QIthIWS6CxSb0s9YYem1O2YRaDqhEGRkcTH1L8KzU7K4sr4o37/or4eL7PzDqtnYgQ4zLt3I53A8AlUKiKqpKvXkIRXj9+nJsbNqq0obG+18+qbWgAqCetwsMZADsy1xwpv1b5COIaV5g0elxh0hqUKsoU14H93YcuXnRDz40bRq/Jz49erkpde9rylxzLYjmjscaGpb7xdW12dszjplmoGa1sFhrKcU/pK2l7VLkydRfDt2/jvJgY+kZFEeMK4YLYOH66O5U2YWHEhYSwIS+XcXFxpcrwx9reE+ceaXbO6GoeCqruPHBXalXWTWmL+qpqXnX7LqKDt3n/G/KHeSM0bE115Bw/3rbfsqVXDVq9+pIdWVmNFqpSGCAVq8sPwVbAYqkJrLFhqW98HmwFSiNEhI8TOzCnU2fW5+axLd8s3XBXkyZ8nNiBPzRvwb+OHuHXTZvxQVoav9m/j6nHjpYrd02fSfOONU0aHQgdve4DO6lknFehNz8+s/DYqkD0X0QMkS1uyh+R1N7TbC6Kut1uXnnlFaZOncpLL73EnDlz/Lbdt28fjz32GJs2mTQWhw9Fdr7n14eH3XhDmnz0oWujKhkADz98kKNHSzcMa5jvg9GpxVLTWGPDUt/4Cqi1u0iTkFCOuE13R9xuGoeUfa9uEBLCoOhofsguPtyyKc84BxLDw/k0I51nW7dhW34+qQUFfmWt6nf/vOONe1Zp1klpeAt3lW/dlML6Ewuiyq9VOVxIyPmFfUaPKey9KsQVcvS2225j4sSJ3H333ezYsYO9e/ee1sbr9fLdd9/RqdOpNfg2bNhA3759ueuuX4R+/nl+r8WLrtMPZkatT2wfntW0aa3Hz+8YN3bH+tru1GKpDayxYalX9NickkYtjouPiY3lk3Szdtgn6emMjY09rc5xt5sMjweAPK+XxTnZdAwvnnvs+aNHuKdpU9yqeJ0oAxdCnvf0ZIsKuuKcB+anNewSMEMDwOveU6VF7PbmbOnnVc/pd/8A0MnbYsANBcML48Ki14IxKDzOuSzJsmXL6NGjBzExp6a3ulwu3G43Ho8HEaGwMDT+669zkjp0uDt8d2qfBV6va0dN6O2HT2qxL4ulVrHGhqU+8kVNCH1w/z5u2L2b1IICxuzYzodpafy8SRMW5WRz0c4dLMrJ5mdNTI6rw+5C7t67BzAej9v3/MgVu3Zx7e5UzouOYbSPUfJdZia9I6NoHhpGg5AQ+kZFcfmuXQB0jyyeyVIRXTHg9wsy4jtWKb9FWajneFWXXJe92Vu3B1QZH2KJbHV97rDeU59/Kfupp56iY8eOJCQkFKuTkZHB5s2bGTiweAb1pKQkduzYwdtvv82oUaNYvnw5ffr0ISwsMvzHH/sOX7jgxo5btgxdVlgYXhvrlHxckUoikiAin4rINhHZISLPiUi4iPQTkUt86iWLyIM1p67FUnHs1FdLvSOle4+uwJZg6xFoFPEuHzh5UVZswvCAy1ZPYX7acwqUufy8P2JC4/dNSLi7lVRiDZaqsDJvy+p7P348acKECaHNm5+aODNz5kyGDh1KQkICn3zyCV27dqVnz+KzX3Nzc/nggw+47rrr+Oqrr8jLy2Po0KG0bdsWgPiGBzZ26bIkMzIya7BUefqtXw4BrceN3VHmuiBiMr4uBV5W1Tecxe7+AxzHrHA6UFV/7dRNBrJU9R9VUUhEQlS1dDeRxVJJrLFhqZekdO+xAegVbD0ChSKepYP+tCQnplWFc2BUBq97/5aCzPe6VUfG5e1+vSoyJKZ/oHTyx+PzXsrcHn4oa8SYka2Kyp577jmKfutycnIICwvjsssuo3v37ifbffXVV3Tv3p1jx46hqiQlJfHee+9x2223FZMfHZ2W2qXr4j1xcUcHiVDxRVLK5vlxY3fcW14lERkH/FlVR/qUNQB2A4WYZdb3AX8DegDtgI7O/3+q6r+cNjcD92KMx6XAr1TVIyJZwDPAhcADqmqn4loCgs0gaqmvvA48HWwlAoFXXO6lgx5enhvdvEYMDQBPYephoFrGxua0pXn9mowNkEanOJaTRqgrhPjIOHIL81m9d1PcxCHXR3s9MfP2uo6PRJD77rvvZP0iz4avoXHs2DGysrJITEzk4MGDhIWZhW3d7tNjiXNyGiauXXNxYlhY7pHOXZYubdJkTx8RqptW9J0K1usFrPQtUNUMEUkF3gC6lvBsdAfGAHHAFhF5GegMXAcMU9VCEXkJuAl4E4gBNqjqI9U8HoulGNbYsNRX3sI8/VVpWKCu4BVX4ZLBf16ZF9V0aI32U7i72i7QbZmr+vdtPCZNyl7xtNIczjrGb2b9FY968KpyWfcxXNB5eMhbyz4dta9wf2qbEV3jKccYmD17NmPHGkOoyKOxdOlSRo8e7bdNYWFUs5RNo0e5XIXZiR3WzG/VaktHl0sT/Dbwz/ZxY3csrWBdgVITkfkrn6Wq+UC+iBwGWgDjgAHAcmcdvijgsFPfA86CLRZLALHDKJZ6S0r3HjOBq4OtR1XxSkjB4iHJa/IjG9f44nJ5J17YCAXVHnYa2+qm+c0iEwIevFoWGZK77+PwpScKxdO7Znvyetq02by0feLaxiEh7u7l1z/J4+PG7qiQJ0FExgOPlDKMsgt4GOjpL2ZDRDYAlwKXAa1V9f9KkZ+lqqdPmbJYqomdjWKpz7wWbAWqiscVmrfo3MfW1Yahoer1QEHHQMhad3xepdKdB4IGGtXmlvyR3Vp7Gs2r2Z5cIfv29Txv0cIbuqdsGrmqoCByZflt8GKGPyrK90C0iNwKJogTMxw4DRNkWpH0st8DV4tIc0dGYxFpXwkdLJZKY40NS33mG+DHYCtRWTyusNzF5z6+sSCi4cDya1cf9RzZiXG1V5uj+Xu7u72FtT4TyIUr7JLC/qOGF3ZfipJe0/0dPdq+/9Il1wxYs/qiLTnZ8YtU/SaS+3Tc2B27KipXjSv6SuAaEdmGWVgwD/gjMAfoKSJrROS6MmRsAh4CvhGRdcC3QCt/9S2WQGCHUSz1mpTuPZKBPwdbj4ricYVnLzr3sa2F4XEVXm69urhzlyx05y0KWPBp/8bj53eJH1CrQym+pEvO3k/Cl2UUiqfWVn6NjMzc26Xr4p3x8YcGiBDjs2vUuLE75teWHhZLsLDGhqVek9K9R0sgFYgop2rQcYdEZC069/Ed7rCYvrXZb37m+/PVvTdgxkG4K+r4Fe3uiRWRoAXnevAWfBW2evGBkLSAZlktj9DQ/LROnZavbdZ8V08R9o4bu6PGpwJbLHUBO4xiqdf02JxyEJgebD3Kwx0SmbHo3L/sqm1DA0A9RxoGUl6BN7dxljstoIuzVZYQXOETCgeMOq+w25LaGFYpwu2OaLhly/BRCxfc2GDH9oFP1Fa/FkuwscaGxQJ/x0z5q5MUhkalLxz6lz3usOik2u5bVRXND0hwqC8bTyyoE9Pue3oSzr264Nz0UA1Jqc1+VUP27d/f49Pa7NNiCSbW2AgwIuJxArSKXpOd8rkiUumAvpLrHQRAv9Yi8kEl6k8TkV3Osax1MhieVfTYnLIDeC/YepRGYWjMiUVDn9jvCY0KSrZT9R5NBQI+FXJ39qb+XvUeKG3fK8vfZ9yrtzLutduY9Nmj5Lnzi+1Py8vkZx/9ifNfv51L3/wFm4/sBExyr5++PYlxr93GV1t/OFn/zg//j4OZ/hesbagx7W7JH9mphTe+hmerFOPJ5OTkoKxhb7EEA2tsBJ5cVe3n85pSTXn9gFKNDRGp9NOhqu5X1crmlvidqvYD7gemVrbP6lKV46wCj1PHvBsFYbHHFg59/LAnJKJHsHTwFu4u1SAIAK4DOTu2liw8kHmEN1Z+wBe3vcL3d03H6/XyWcrsYnVeWPwWvZp35ts7p/HPCX8i+ft/AfDppu+4uvdFfHrzy/x72bsAfLt9IUktutIyrmmZyoTgCr+sYOCooYVdF6NkBOog/bAPM1XVYqk3WGMjCIjIBSKyWERWichMEYl1ygeJyCLHg7BMROKBx4DriqazOSs5/kdEvgHeFJH2IvK9iKxz/rdzZE0TkX858naKyNVOeaKT3AcRCRGRf4jIeqf9PeWovhho49P2KRFZ7rS92ylvJSLzHX03iMgIp/wGp58NIvKkz7nI8tm+WkSm+ej/jIjMAZ4Ukc4i8p1zblaJSCen3u98dHjUKYsRkVlO3Q1lTQMsosfmlC3Au+XVqy3yw+KOLDr38ePekIhqpQivLt7C3YU1JXvtibmJWkqEutvrIc+dj9vrJtedR4vYJsX2bzuayrDEAQB0btKePekHOZJ9nNCQUPLc+RR4ChBx4fa6eW3FTCYOuaHCOvXytB16VcG5J0LVtbmah1cWf09OTi6oQfkWS53DGhuBJ6rEMEqxG52INMXMcR+vqv2BFcBvncj8GcB9qtoXGA9kA48AMxwvyQxHzADgclW9EXgBeFNV+2DWV/iXT3etgOGYrIGleVh+AXQAzvFpXxYXAZ8423cB6ao6CBgE/FxEOgA3Al87npC+wBoRaQ08CYzFeGoGicgV5fQF0BVznh5wdHvROTfnAQdE5AKgCzDYkTtAREY6eu5X1b6q2hv4qgJ9gZkCm19urRomL7zhocVDH8/whoR3CbYuXs/h+JqSnVl4vH2BN3etb1mruGbcPfh6zn35Gga8cCVxETGM6lA8b1mP5p353xYzW3T1/k3sSz/EgcwjXNFzPPN2LePmmb/jt8Pu4M1Vn3BVrwuJCqvcWmmNNKb9LfmjOjT3NqiJKalbgZdrQK7FUqexxkbgKTmMMqPE/nOBnsBCEVkD3Aa0xyxydUBVl4NZXElV/Y3pfqaquc72UOC/zvZbGOOiiE9U1esk8WlRipzxwNSiflT1uJ/+nhKRncDbwF+dsguAW51jWAo0wdz4lwN3OKmSk1Q1E2OMzFXVI05f7wAVmUo501mJMg5oo6ofO3rmqWqOo8MFwGpgFWbRqS7AemC8iDwpIiNUtUKzDXpsTtlJkBdny4todGDxuY/meF1hnfzVmbP+Q554/y7+8v6dzFlX+jIWW/ev4W8f/IK/vH8n//zsNwBk5qbxzKf38cT7d7F216nFPP/91cOkZfuJadDcDtU4nHLZkr482/d9Wl4m32xbwKKJM1gx6WNyCvP4aOM3xdpMOvcm0vMyufCNO5m26iN6tehCqCuEBhGxTL/m73x52yv0btmV73Ys4pJuo/j9//7O3R8/zMp9GyqsVwiuiJ8UDBo5pLDLIpTMwBwtAA8mJyfXmLfIYqmr1ImI8HqGAN+qajHfroj0ofSFlEoju4x9vjJ8n9LFjy4V6fN3wEeYJamnYzwrAtyjql+fJtR4FyYAb4nIU1DmGLhv/yUfQYuOszTdi8r/pqr/LkWHAZhYl7+JyDeq+lgZOvjyV+BWoCoLalWL3Mgm+5YMfsStrlC/N/j9x3exKOVLfnfli4SEhPHSl5Pp1X4IzeNPqZuTn8X7PzzHry6ZQuO4FmTmngBg5fbZDOl6AQM6jeGlLyfTt8Nw1qcuom3TLjSMOT2mwes5/iNmafIaY2vGinOSGo3McNb3YEHqCtrGt6JJdEMALu46khX7NvDTXhecbBMXEcMzE8yyHqrKeVOvo2188QSY/1w4jXuG3sqnm74nqWU3rug5nrs++iPv3/BcpfRL8rQ7r4238a5Pw5cf8Ii3a3WOFfg2OTn582rKsFjOSKxno/ZZAgwTkc4AIhItIl2BzUBrERnklMc5gZGZlL3ewSLgemf7JmBBGXVL8g0wsSgAU0Qa+6uoql7gOcAlIhcCXwO/FJEwp21XJ1aiPXBYVV/BrD3SH+P5GCUiTcWs5XADUBT5f0hEeoiIC5OGubS+M4C9RUMvIhIhItGODnf6xLy0EZHmzrBNjqq+DfzD0aFC9Nickg08WNH6gSInqtmeJYP/rOoKLXONioMnfiSxRQ/CwyIJcYXQuVWfYl4KgBXbv6dvhxE0jjPOrLgos+BpiCuUQnc+bk8hIoLH62HO+o8Y3/faUvvyFu7eH4hjKwuPuqOPFxw8OZTSpkELVu/fRG5hHqrKwt0r6dKk+ClJz8ukwGOcA++u/YIhbfsSF3EqKeeu43s4lHWUoe36kevOwyWCIOS7qxYm0VhjO9ySP7JdU2/cD+XX9osb+E012lssZzTW2Ag8JWM2isVKqOoR4HbgXTHrEiwBuqtqAXAd8LyIrMWsVxBJ+esd3IsZtlgH3ALcVwldX8WsDbLO6fPGsio7wXx/AX7vtN0ErHICTv+N8ZSNxsRprAauAp5T1QPA/znHshZYpapFOQYmA18As4GyZj7cAtzrHOcioKWqfoMZQlosIuuBDzCGWRKwzBni+ZOjc4XpsTllBjC3Mm2qQ3ZUi91LBj8coq6Qcr0prRsnsv3AOrLy0ikozGPjj0s5kXWkWJ3DaXvJyc/kn5/9lic/nMjSrWYYYmDnsaTsXcGLX07mkgG38cPGTxnc9XzC/cQ0eN2ptRK/su74vJNG7jmte3JJt9FcPO1njH/9dryq3Nj3Mt5a/SlvrTaXzPZjuxn32q2MfuVm5uxaQvL4e4vJ+/v8V/n9iJ8BcHmP8cxc/z8uf2sidw8uN07YL6GERF5RMHjEoMLOC9EyPYv+eDY5OXljeZXk9KnziRUR7hv4HQhEJNWJLyutfL3z2iQifxGRWsm+KyIT5dQCdLc7DxU10c80cQLqfcpOpgxw+n6hJvqugF5FaQhWicjQ2tahOth05RZLKaR075GEiQUJqcl+sqJb7Vo26I9RiKtlRdss2vwl8zd+SkRoFC0btSc8NIKrzvvVyf3vL/gXPx7Zyj2XPkWhu4CnP7mHiRc/QYuGbU/WycnP5PXvHufnFzzKh4teIic/k7F9rqFjy1PpPPLSpq5EcwYE6FDL5Or2D+wIcYX6jVOpSxyTzJ2fha/weMRb0QDe3UDP5OTknPIqShWXeHeMki+cgOiK1A9RVb9TvUUkFRioqkf9lTsexf8Ahap6W2V1rg4iMhd4UFVXVKJNmcfsU28a5lyWmo9IRG7HnINflyFDMPdXb03o5QTH/8MJ7K+0rDL6CC0jVrBaWM+GxVIKPTanrKeGg0UzY9rsWDbojzGVMTQAzut+CZOv+je/ufyfxETE0Sy+TbH9DWOa0aPtICLCooiNiqdzqyT2HdtZrM7/Vr7FhefcxIrts2nbrCs3jf4dny9/rXhHmlNry46nZm3YW1t9VZcmGtfxlvyRCU0qPqzy64oYGv4QkQEiMk9EVorI1yLSyqd8rYgsBib51Pc3LX20iMwRkf9igqgRkU8cuRtF5BeV0UtVs4CJwBVFQ7BS+lT0RBHZLCLTnfIPnGFQRGSciKx2PCWvF3lJRGSK4zlZJyL/cMqSReRBx+swEHjHecqPKkNOqog8IiILgMkicjJNvoh0EZGVFfwMSnqO2orIVyKyRUT+7FMnRURewgSstxWRl0VkhXN+H/WRV1295gNFQ/G+sq4R/2kG7hKRrWISTL5S5J2R09MMDBaTMmG187+bU+9253r53PGw/FpEfuvUWyJlDMODNTYslrJ4GDPsE3Ay4tptWz5wcgPE1byybYsCPo9nHmJt6gIGdh5bbH+fxPPYcWA9Hq+HgsI8Ug9vpmWjU3Geh9P3kp59jC6t+1LgzkOcv0KfmAavJ30/cNKNPn/LTp76ah5PfTWPtxevptBT/OEpp6CQaQtX8PTX83nuuwUcSDcTOLLy8nlh9iKe+moeG/YdPFn/jQUrSM/NO/l+Q9qCnjX1RFUThBISdWXB4BEDCzsuQCnLkPggOTn5i0qI9h2G/VhMTNTzwNWqOgB4HShaU+UN4F5VLelO9zctHcw08T+patGKt3c6cgdihimbUAmceKpdQBfxPxUdzGy7/zhP4hnAr0QkEpPc7DpVTcIMw/7SuWldCfRy6hcbBnU8DiuAm5wp9lqaHJ8meao6XFWfANJFpJ9TfgdVT642GBMj1w9zgy/KDt0Nk4rgHFXdjTnXA4E+mLg1X09EdfS6DMdg9JWFMUJOSzMgZsjpYcxsyPMxM/d88U0zsBkYqarnYFIv/NWnXm/McPtgzHWY49RbjAms94udjWKx+KHH5pSClO49bsL8sFUuWUMZpDfosGXlOb9tirgq9cNexKvfJJOdl0GIK5Rrh91LdEQcP2wykxxG9LyMlo3a07PtIP4282eIuDiv+yW0bnxqgsvny17nssF3AiaO4z9fP8LcDR8xYeDtJ+t43bv3Aq0B0nPy+GF7Kr+/cBRhoSG8uWgVa37cz6AOp4Zlvk/ZTuuGDbh92EAOZ2Tx0aoNTBx9Lqt/3M/A9gn0a9eaV+cvo3eblmzcf4g2jRoQH3XqlOZ5spvluDOWxYTFF0+qUcfp5+kwPMHbdMfn4SvwiLfkMNBe4O5Kisx1bqAAiEhvzA/8tyICZljvgJiEfw1VtSjQ+i3gYmf7AqCPnIo7iMcYAQXAMlXd5dPfvSJSFJjd1ql3rJI6F80W852KDibNfRdMXNgeVV3olL+NiTX7FtilqkWZZKdjPDQvAHnAqyIyCxPTVRbd/Mj5p/PeN/3Aq5gYt99iYuSqer19q6rHAETkI0zKgU+A3aq6xKfetY7HKBST96gnsK4aej0lIg8BRzBGZRFFsk6mGXB0800zMK8ovYGIzMQYGEXM9Bl+iQemi0gXjCEX5lNvjpPOIFNE0oGi2VXrMQaVX6yxYbGUQY/NKRtTuveYzKkfrmpxIr7zptX97m+NSMOqyvjN5adP3xzR87Ji78f3u47x/UoPiLzr/EdObsdFNeKBK54/rY63MDW32HuvUujx4HIJhR4PDaKK216HMjIZ170zAM0bxHIiO5fMvHxCXC4KPR48Xi8i4PF6+WHrLu4cPui0PjemLWJws4tPK6/rNNW4Tjfnj8z5PHzFguOurKI8N17g5uTkZH+5ayqKABtLei/EXD/+Au5KnZYuIqPxmTbvvB8PDFXVHDFxEJUyqsXkwEnEJCsrdSq6mHiSkroqfqa0q6pbRAYD4zAz7X6NeVL3q0Y5avoG9H6ISd43G1hZZDBUgdKOp1hfjjfpQWCQqp4QE3Phe36rotfv/MSSVCRNQFn46vI4xqi40vns5vrs8w0a9/q891KOPWGHUSyW8vkXZppwtTjesOvG1f3ub1MdQ6O28LoPRhdtx0dHMrpbR/4yazaPff49kWGhdGvZrFj91vENWO8Mk/x4LI0TObmk5+RxTrvWbDl0hFfmL+OCXl1ZtH03AxITCA89Pe42NWt9f1Xv4Ro+tBohjJDonxYMGd6/sMMClFzgr8nJyYFY2G0L0EycmQciEiYivVQ1DeN6LzJubvJpU+q09FJkxwMnHEOjO8bFXmHEBIi+hEkeeAI/U9Gd6u3k1OyJGzBT9DcDieKkAcDMOJvntI9X1S8x6zH1K6V735QApcopTWdVzXP0fBkzDFVVzheRxiISBVwBLCylTgPMTTxdRFpwyvNUk3r5SzOwzClvJCbVwVVlyIjHrN8DZuZkQLDGhsVSDj02pyjmS1fVpyCONeqxbk3fe9ti3N91H80+GeSRU1DIhv2H+OMlY3jksnEUuD2s3F08nnNsj07kFBTyzDc/sGB7Kq0bNsDlEqLCw/jZiMHcf/5w2jSKJ+XAYfq0acnM5euYvmglqUdPnOoSDT2Ym1qrS70Hmv6ejsMvKxgwC3i03MoVwJkSfzUmcG8tsAaTrh/M2P6LYgJEfT1R/qall+QrIFTMdPLHMdPwK8IcR+4yzBDJ3Y6u/qaiA6QAtzl9NQZedm6wdwAznfpezEKPccAXTt15lJ6fZBowVcz0dvEjxx/vYDwRZT1A/FtE9jqvxaXsX4AZuloDfFjarBhVXYsZUtqIibUpzSCprF5l4i/NgKruw8ReLAW+w1wf/jIr/x2TDHEhAZyNZ6e+WiwVJKV7j/OBL6nk8OPRJr3Xrus9sRPOE19dR71Zh/PT/3MycHXtngNsOXiYawf1BWBF6l52HzvBVQOSSm+vyl9nzeGBC0cQGXZquPfT1Zvo3aYFRzKz8arSv31r3liwgl+OOTVCEB/WbOdFCXd2rKljqwUOAf0Tpoyo8YRoZwpSyWm5NY2IPIjxnDwcbF18qWm9RCRWVbMcz8bHwOtFS0DUBtazYbFUkB6bU76lklkgDzftu3pd74ldzhRDA8BbuHu37/uG0ZHsPpZGgduDqrLt0FFaNCh+OLkFhbg9JqXA0p176NiscTFD40hmNhl5eXRq3sTEfpg0BLi9xdMQpBce6VjgyQtYcqpaxgPcYA2NuouIfIyZNVG5vPU1TC3plex4gjZgZhB9UoN9nYb1bFgslSSle4+XMfkFyuRQswErN/a8oydmXPeMoSBr1jxv4ZZRvmVfb9jKmj37cYnQplE81w5MYtkuM5RyXuf2pB49wXvL1iAitGgQy7WD+hIdfsrYeHPRKi5O6kazuBgy8/KZtnAFeYVuLuzdlT4Jxdc16d1wxIJejc4bzpnHxIQpI05bp8disVhjw2KpNCnde4Rigrn8RsgfaDFkeUr3W/pQS6mcA0l++qtL1ZsxJFj9h0p45k/b3+/yE9RYV3kyYcqIycFWwmKpq9hhFIulkvTYnOIGrgG2l7Z/f6vzlqZ0v6XvmWhoAKg3s9ZXvPXFrQVxaQWHV5dfs84wAxOUZ7FY/GCNDYulCvTYnHIcuBSTXOcke1uPXLK56439EQkPjmbVQ705x0HblF+zZll/Yn6DYOtQQRYAtyVMGWFdxBZLGVhjw2KpIj02p2zBZExMA/gxYcyirV2uHYiT3+BMxOvekxpsHQAO5O7s41FParD1KIeNwBUJU0bUyuq4FsuZjDU2LJZq0GNzyhrg4tR2F8ze3umqIZhpZWcs3sJdmcHWoYgfszbtLr9W0NgIjEmYMqLKuVcslvqENTYslmrSY3PKkp0dL09GJLf82nUbr3t/nRn+WX/ih27VWS67BikyNI6UW9NisQDW2LBYAsKkqWN/wKQjzgq2LtVBvRlBj9coIteT2TLXk7Wq/Jq1ijU0LJYqYI0NiyVATJo6dgFm+eajwdalKqg3Lx28bcuvWXukpC2uS56N9VhDw2KpEtbYsFgCyKSpY5dg1q7YEWxdKovXvXcX5a8OWavsyFw7oBorcwaS74Dh1tCwWKqGNTYslgAzaerYbcBQzEJVZwzewl3+FmYKGoo37HDej8FOX/4GcEnClBEZQdbDYjljscaGxVIDTJo69ggwBvgs2LpUFK97X52csrv2+JzWQez+kYQpI+5MmDKiMIg6WCxnPNbYsFhqiElTx+YAV1LHFn3yh3rTWwZbh9I4UXCoS6E3f1Mtd5sP3JowZcTjtdyvxXJWYo0Ni6UGmTR1rHfS1LH3A9cCddYNr1qQBZ7EYOvhjx0Za2ozbiMVGJYwZcRbtdinxXJWY40Ni6UWmDR17ExgAFAn1/zwuvftog7/HmxKX9xHVWsjj8ksYEDClBEra6Evi6XeUGd/XCyWs41JU8duxwSOTg22LiXxFqaeCLYOZVHozY/PKDxWk4ZaAfBb4LKEKSOO12A/Fku9xC4xb7EEgRcnzr4OeBFoEmxdAPIz3lyonqPDgq1HWSREd109rMWV59SA6A3A7dabYbHUHNazYbEEgUlTx84AegDvBlsXAPWkNQ+2DuWxN2drP6969gZQZAGQDPSviKEhIi1F5D0R2SEim0TkSxHpWkb9RBHZ4GyPFpEvqqKkiNwvItE+778UkYZVkVVCbrKI7BORNSKyTUQ+EpGe1ZVbSj/TROTqQMutDs7ncV6w9ahPWGPDYgkSk6aOPTJp6tgbMUvV7wmWHqruPHB3CFb/lUD2Zm/dHiBZSzFGxqMVmdYqIgJ8DMxV1U6q2hP4I9AiQPqUxf3ASWNDVS9R1bQAyX5WVfupahdgBjBbRJoFSHZdZjQm+Z6llrDGhsUSZCZNHTsL6Am8AHhru3+ve/8O4IxYrXbdiXldVLU65ygDE5txXsKUERsr0W4MUKiqJ+NtVHWNqv4ghqdEZIOIrBeR68oSJCIxIvK6iCwXkdUicrlTHiIi/3BkrBORe0TkXqA1MEdE5jj1UkWkqbP9W6ffDSJyv1OWKCIpIvKKiGwUkW9EJKq8A1TVGcA3wI2OnAEiMk9EVorI1yLSSkR6iMjJZHVOX+v81S/l2Mc5x7zeOQcRPsf0pIgsc16dnfJpIvKyiMwRkZ0iMspplyIi03zkXiAii0VklYjMFJFYH7mPOuXrRaS7iCQCE4HfOF6dEeWdG0v1scaGxVIHmDR1bNakqWPvAQYBc2uzb29h6hkTEJntTm+T58leU4WmbuAloHPClBHPJkwZUVmDpTfgb6jlp0A/oC8wHniqtButD38CZqvqIIwR85SIxAC/ADoA56hqH+AdVf0XsB8Yo6pjfIWIyADgDmAIcC7wcxEpimnpAryoqr2ANOCqCh7nKqC7iIQBzwNXq+oA4HXgCVVNAcJFpKNT/zrgfX/1S+gbCUwDrlPVJIyB+0ufKhmqOhhjdP/Tp7wRMBb4DfA58CzQC0gSkX6O4fUQMF5V+wMrMAZlEUed8peBB1U1FROkXeTV+aGC58ZSDayxYbHUISZNHbtq0tSxY4CfALWSyMrrDtoITpXYnL40v5JNPgN6J0wZMamG1jYZDryrqh5VPQTMwxiN/rgAmCwiazCGZSTQDmOoTFVVN4CqlmcEDgc+VtVsVc0CPgKKntJ3qeoaZ3slkFjBYylaG6cbxsD61tHzISDB2fc+Jm8MGGNjRjn1i+jm6LXVeT8dGOmz/12f/0N9yj9XM5NhPXBIVdc73q2NznGdi/EMLnT6vg1o79P+I+d/Zc6DJcCcEa5Ti6W+MWnq2M9fnDh7FnATJoixY9ktqo56jp9RY/TbM1b379d4bFoFgiQXAX9MmDJiXgC63Qj4C3Ks7OJ1AlylqluKFZq4kMpMDyyrX1+DzAOUO4zicA7GMyDARlUdWkqdGcBMEfkIUFXdJiJJZdSviL5Q/Nh9t4uOxUvx4/Ji7mEe4FtVvcGP3KI2Huw9L2hYz4bFUkdxso++BXQHbgfWBroPVU8BFNaYIVMTePFEHM3fu87PbgW+AEYkTBkxLECGBsBsIEJEfl5UICKDRGQUMB+4zom5aIZ5Wi9rEb6vgXsc4wKfoY9vgIkiEuqUN3bKM4G4UuTMB64QkWhnGOZKoMpDAiJyFcbr8i6wBWgmIkOdfWEi0gtAVXdgbtwPYwwPyqrvw2YgsSgeA7gF4wUq4jqf/4srofoSYJhPnEe0lDFLyMHfObXUENbYsFjqOJOmji2cNHXs9ElTx/bDuNr/R+WegP2inoM7gfBAyKpN1h2fV3KqbiHwJpCUMGXEZQlTRiwIZH+OG/9K4HwxU183YjxO+zGzVNZhjMHZwO9V9WAZ4h4HwoB1YqbGFq2/8irwo1O+FidQE/gP8L+iAFEfnVZhYiCWYWbXvKqqlU18VhQkuQ24GRirqkdUtQDjyXnS0WUNxWdvzHDqv+/oUl59VDUPE2MyU0TWYzwTvgnuIkRkKXAfJj6jQqjqEYwx/q4TrLoEY6CXxefAlTZAtPawSb0sdR4RuRIz7tpDVTcHWPYiVfU7BU5EvuTUj/6NqvqSU94a+JeqBiV/wIsTZ/fA/CDfCMRUVU5h7sIFnrylwwOmWC1yVfvfbAl1hcdgghFfSZgyIpA5OCy1iIikAgNV9WiwdbHUDNbYsNR5ROR9oBXwvaomB0mHROALVe0djP798eLE2UXu85sxXo+QyrTPz3h3vnoOjCy/Zp0iH/isS4MBr/RvMn52wpQRnmArZKke1tg4+7HGhqVO48yX34KZIviZqnYXkRDgSeBCzHDCK6r6vIhchJkydxQzha+jql4qIslAlqr+w5G5AbhUVVNFJEtVY52pijOABjhT8pwcCqnAQMx0vMsdXb7FpBr/QlV7O1P6XnbquYHfquocEbkdM6skGuiEmTnwe0f/15z6Cryuqs9W91y9OHF2C+AGTFDpwIq0yTvxwkYoKDm2XhfJBb4HPgU+eGDGF2nBVcdisVQGG5lrqetcAXylqltF5LiI9MfkFSjKR+AWkcbODf8VzHz87ZwKXKsoNwJfq+oTjjEQXWL/ZKC3qvaDk56OIiYBqGqSiHQHvvEJUOuHifDPB7aIyPNAc6BNkZekArMqKsSkqWMPYYytf744cXZ7TLDfhcA44LQ+VL1uKOgUiL5riMOYYM/PgG8fmPFFTpD1sVgsVcQaG5a6zg2cSvDznvO+IyXyEYhIP8wc/m0AIvI2JklSRVkOvO4kJ/rEJ0dBRRiOSWiEqm4Wkd1AkbHxvaqmOzptwsz/3wh0dAyPWZhZCAFl0tSxuzHG1ysvTpwdAgzmlOExAIhSz+FdmORPdYV9wEKf1+oHZnxR6xlVLRZL4LHGhqXOIiJNMJ6K3iKimHgExSTnKW38z9+YoJviM68iT2uoOl9ERgITgLdE5ClVfbOiqpaxr2S+g1BVPSEifTE3/0mYBEl3VrCvSjNp6lgPZirhYiD5xYmzQ4HeXs/hXpgZA32BPtTeVEAvsBszFTIF83kufGDGF7trqX+LxVLLWGPDUpe5GnhTVe8uKhCReZh4jIkiMrdoGAVz4+ogIp2cPAC+CX5SMYud4QzDnLbomIi0B/ap6itOzoL+mKmURZQ1L38+Jk5itjN80g4T29G/tMpOeuUCVf1QRHZgpi/WGpOmjnUDa2DsGrj/naLyp6+7tDkmw2J753/RdmNMLEsDzDmIo/RAVDdmCmoGcMx5HQIOOK+dGONi6wMzvsgL9HFZLJa6izU2LHWZG4ApJco+xCzNXpSPoBATIPqCiPwCmCUiR4EFmPTJRW1udVIZLwe2cjqjgd858rKAW313quoxEVnoBJf+DxMgWsRLwFQnd4AbuF1V852cTaXRBnhDRIq8Lf9XxjmoNR6Y8cVhTJxEWQmpAHj6ukujMR4dN+B+YMYXdkaIxWLxi52NYjkrEZHRmEWXLg2yKhaLxVLvsRlELRaLxWKx1CjWs2GxWCwWi6VGsZ4Ni8VS44hIVillE0XkVmd7rohUKBFZgPX6k4hsFJF1zjoZQ6og43Ynfb3FYvGDDRC1WCxBQVWnll/rFCISWpRbpZx6IapabsCqs0LppUB/J6C3KVVblO52YANmUTaLxVIK1rNhsViCgogki8iDPkU3i8giEdkgIoN96vxHRL4B3hSRRBH5QURWOa/znHqjRWSOiPwXWC8ij4vIfT59PSEi95ZQoRVwVFXzAVT1qKruF5FxIvKxT9vzReQjMUvIT3P0Wy8ivxGRqzGp4d9xPCNRIjJAROaJyEoR+dpJhV/kvXlWROaLSIqYJeo/EpFtIvIXp06MiMwSkbVOP9dhsZwFWM+GxWKpK8So6nlOcrXXOTV1eQAwXFVzRSQaOF9V80SkC/Aup9aBGYxJKb/LSSf/EfCcM8X4eme/L98Aj4jIVuA7YIaqzsMsE/+iiDRzli+/A3gDk3q+WJp5VU0TkV9jZj6tcDLQPg9crqpHHGPhCU4lbStQ1ZGOIfSpc2zHgR0i8ixmCvZ+VZ3g9BFf3ZNqsdQFrGfDYrHUFd4Fk80VaOCzZsxnqprrbIcBrzg5TWYCPX3aL1PVXY6MVOCYiJyDWSNmtaoe8+1MVbMwN/tfAEeAGSJyu5qo+bcwnpaGwFBMbpWdOGnmxSz6l1HKMXTDGEnfOnldHgISfPZ/5vxfD2xU1QOOZ2Un0NYpHy8iT4rIiKJU9xbLmY71bFgslrpCyalxRe+zfcp+g8lK2hfzsOSbidS3HsCrmHiKlhhPyekdmtiOucBcx4C5DZPR9Q3gc0f+TCdWpCJp5gVjRAz1c4xF6eu9FE9l78Wkst8qIgOAS4C/icg3qvqYH1kWyxmD9WxYLJa6wnUAIjIcSPfzVB8PHFBVL3ALpadNL+Jj4CJgEPB1yZ0i0s0ZiimiH2bNFlR1Pybg8yGcdPJOAKlLVT8EHuZUOnrfVPZbgGZO8CkiEiYivco86uI6tQZyVPVt4B/4SXlvsZxpWM+GxWKpDaJFZK/P+2dKqXNCRBZh1mDxtzDdS8CHInINMIfTvRknUdUCEZkDpPmZnRILPO8MlbiB7RRfKfgdoJmqbnLe+0szPw2Trj4XM+RyNfAvJ94iFLNq8UZ/epYgCXhKRLyYdWZ+WcF2Fkudxib1slgsZyWOUbAKuEZVt1Wh/QuYWI/XAq6cxVLPsMMoFovlrENEemI8Fd9X0dBYCfQB3g60bhZLfcR6NiwWi8VisdQo1rNhsVgsFoulRrHGhsVisVgslhrFGhsWi8VisVhqFGtsWCwWi8ViqVGssWGxWCwWi6VGscaGxWKxWCyWGsUaGxaLxWKxWGoUa2xYLBaLxWKpUayxYbFYLBaLpUaxxobFYrFYLJYaxRobFovFYrFYahRrbFgsFovFYqlRrLFhsVgsFoulRrHGhsVisVgslhrl/wGGFCZuPP4jNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sca_df={'Function':['Cataloging', 'Processing', 'Archives and/or Special Collections',\n",
    "                    'Electronic Resources', 'Acquisitions', 'Collection Development', \n",
    "                    'Library Systems','Federal Depository Library Program',\n",
    "                    'Other', 'Interlibrary Loan'], \n",
    "        'Number of Units Including Functions': [30, 24, 22, 15, 14, 13, 13, 5, 5, 5]}\n",
    "\n",
    "x=sca_df['Function']\n",
    "y=sca_df['Number of Units Including Functions']\n",
    "labels=['Cataloging', 'Processing', 'Archives and/or Special Collections',\n",
    "        'Electronic Resources', 'Acquisitions', 'Library Systems',\n",
    "        'Collection Development', 'Federal Depository Library Program',\n",
    "        'Other', 'Interlibrary Loan']\n",
    "\n",
    "plt.pie(y, labels=labels, autopct='%1.1f%%')\n",
    "plt.title('Technical Services Participation by Special Collections/Archives')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('SC_Archives_Services.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4a60900e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Q1         Q2                    Q3   Q4  \\\n",
      "0        Yes         No        1 Year or more   No   \n",
      "1        Yes  As-Needed  Three Months or less   No   \n",
      "2        Yes        Yes        1 Year or more   No   \n",
      "3        Yes         No            7-9 Months  Yes   \n",
      "4  As-Needed         No        1 Year or more   No   \n",
      "\n",
      "                                                  Q5    Q6    Q7  \\\n",
      "0  Cataloging,Processing,Federal Depository Libra...   4.0   4.0   \n",
      "1  Cataloging,Electronic Resources,Interlibrary L...  17.0  13.0   \n",
      "2  Cataloging,Processing,Electronic Resources,Acq...   7.0   7.0   \n",
      "3  Cataloging,Processing,Interlibrary Loan,Collec...   3.0   3.0   \n",
      "4  Cataloging,Processing,Electronic Resources,Acq...   3.0   1.2   \n",
      "\n",
      "                                                  Q8   Q9             Q10  \\\n",
      "0  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "1                      Lay-Offs,Voluntary Retirement   No             Yes   \n",
      "2  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "3                                          Furloughs  NaN              No   \n",
      "4  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "\n",
      "   ...                                                Q29  Q30  \\\n",
      "0  ...                                                NaN  NaN   \n",
      "1  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "2  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "3  ...                                                NaN  NaN   \n",
      "4  ...                                                NaN  NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0                                     Not applicable   \n",
      "1  I strongly disagree about the success of these...   \n",
      "2  I somewhat agree that these changes have been ...   \n",
      "3                                     Not applicable   \n",
      "4                                     Not applicable   \n",
      "\n",
      "                                                 Q32                Q33  Q34  \\\n",
      "0                                     Not applicable     Not applicable  NaN   \n",
      "1  I strongly agree that these changes will have ...  Somewhat negative  NaN   \n",
      "2  I strongly agree that these changes will have ...  Somewhat positive  NaN   \n",
      "3                                     Not applicable     Not applicable  NaN   \n",
      "4                                     Not applicable     Not applicable  NaN   \n",
      "\n",
      "   Q35         Q36    Q37                             Q38  \n",
      "0   No     Indiana  Other                       Librarian  \n",
      "1  NaN    New York  Other                       Librarian  \n",
      "2   No  California  Other  Manager, Supervisor, Unit Head  \n",
      "3  Yes      Nevada  Other                       Librarian  \n",
      "4  NaN      Alaska  Other  Manager, Supervisor, Unit Head  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "           Q1         Q2                    Q3            Q4  \\\n",
      "44  As-Needed  As-Needed  Three Months or less            No   \n",
      "45         No         No        Not applicable           NaN   \n",
      "46         No         No        Not applicable           NaN   \n",
      "47        Yes  As-Needed        1 Year or more  I'm Not Sure   \n",
      "48        Yes  As-Needed  Three Months or less            No   \n",
      "\n",
      "                                                   Q5   Q6   Q7  \\\n",
      "44                  Electronic Resources,Acquisitions  2.0  2.0   \n",
      "45                                         Processing  1.0  1.0   \n",
      "46  Cataloging,Processing,Interlibrary Loan,Acquis...  1.0  1.0   \n",
      "47  Cataloging,Processing,Electronic Resources,Acq...  6.0  2.0   \n",
      "48                 Cataloging,Interlibrary Loan,Other  4.0  4.0   \n",
      "\n",
      "                                                   Q8   Q9             Q10  \\\n",
      "44  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "45                                     Not applicable  NaN              No   \n",
      "46  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "47          Lay-Offs,Voluntary Retirement,Resignation   No             Yes   \n",
      "48  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "\n",
      "    ...                                                Q29  Q30  \\\n",
      "44  ...                                                NaN  NaN   \n",
      "45  ...                                 Change to training  NaN   \n",
      "46  ...                                                NaN  NaN   \n",
      "47  ...  Change to documentation,Change in organization...  NaN   \n",
      "48  ...  Change to documentation,Change to training,Cha...  NaN   \n",
      "\n",
      "                                                  Q31  \\\n",
      "44                                     Not applicable   \n",
      "45  I strongly agree that these changes have been ...   \n",
      "46                                     Not applicable   \n",
      "47  I somewhat agree that these changes have been ...   \n",
      "48  I strongly agree that these changes have been ...   \n",
      "\n",
      "                                                  Q32                 Q33  \\\n",
      "44                                     Not applicable      Not applicable   \n",
      "45  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "46                                     Not applicable      Not applicable   \n",
      "47  I neither agree nor disagree on the impact tha...   Somewhat positive   \n",
      "48  I strongly agree that these changes will have ...  Extremely positive   \n",
      "\n",
      "                                                  Q34  Q35       Q36    Q37  \\\n",
      "44                                                NaN  Yes  Oklahoma  Other   \n",
      "45                                                NaN   No       NaN  Other   \n",
      "46                                                NaN  Yes  Oklahoma  Other   \n",
      "47  very concerned we will not be allowed to bring...   No  New York  Other   \n",
      "48                                                NaN   No       NaN  Other   \n",
      "\n",
      "                               Q38  \n",
      "44  Manager, Supervisor, Unit Head  \n",
      "45                           Other  \n",
      "46                       Librarian  \n",
      "47                       Librarian  \n",
      "48  Manager, Supervisor, Unit Head  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Other\n",
    "\n",
    "#Import File\n",
    "other=pd.read_csv('Impact_of_COVID_on_Tech_Services_Other.csv')\n",
    "\n",
    "#Data Analysis\n",
    "print(other.head()) #Column names, data samples\n",
    "print(other.tail()) #Tail sample, 49 rows\n",
    "other.describe() #5 rows with 38 columns\n",
    "type(other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8952137e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q6    Q7\n",
       "0    4.0   4.0\n",
       "1   17.0  13.0\n",
       "2    7.0   7.0\n",
       "3    3.0   3.0\n",
       "4    3.0   1.2\n",
       "5    5.0   6.0\n",
       "6   16.0  15.0\n",
       "7    1.0   1.0\n",
       "8    1.0   1.0\n",
       "9    4.0   3.0\n",
       "10   8.0   8.0\n",
       "11   6.0   5.0\n",
       "12   9.0   6.0\n",
       "13   7.0   7.0\n",
       "14   4.0   4.0\n",
       "15   5.0   5.0\n",
       "16   0.6   0.6\n",
       "17   1.0   1.0\n",
       "18   1.0   1.0\n",
       "19   5.0   5.0\n",
       "20   8.0   8.0\n",
       "22   2.0   2.0\n",
       "23   5.0   6.0\n",
       "24   1.0   1.0\n",
       "25   3.0   3.0\n",
       "26   2.0   2.0\n",
       "27   7.0   7.0\n",
       "28  12.0  12.0\n",
       "29   5.0   4.0\n",
       "31   2.0   2.0\n",
       "32   1.0   1.0\n",
       "33   1.0   1.0\n",
       "34   4.0   3.0\n",
       "35   5.0   5.0\n",
       "36   4.0   4.0\n",
       "37  12.0  12.0\n",
       "38   3.0   1.0\n",
       "40   9.0   9.0\n",
       "41  12.0   8.0\n",
       "42   1.0   1.0\n",
       "43   5.0   4.0\n",
       "44   2.0   2.0\n",
       "45   1.0   1.0\n",
       "46   1.0   1.0\n",
       "47   6.0   2.0\n",
       "48   4.0   4.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Staffing Numbers Before and After\n",
    "other_staffing=other[['Q6','Q7']]\n",
    "other_staffing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "691de130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.904347826086957\n",
      "4.408695652173913\n",
      "225.6\n",
      "202.8\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(other_staffing['Q6'].mean())\n",
    "print(other_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 4.90\n",
    "# After: 4.41\n",
    "# Percent Change: 8.84% Decrease\n",
    "\n",
    "#Sum\n",
    "print(other_staffing['Q6'].sum())\n",
    "print(other_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 199.6\n",
    "# After: 181.8\n",
    "# Difference: 17.8 positions lost\n",
    "\n",
    "# Post Data Restructure\n",
    "# Before: 4.90\n",
    "# After: 4.41\n",
    "# Percent Change: 10% Decrease\n",
    "\n",
    "# Positions Lost\n",
    "# Before: 225.6\n",
    "# After: 202.8\n",
    "# Difference: 22.8 positions lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2d3cc737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  0\n",
      "Cataloging                                       44\n",
      "Processing                                       37\n",
      "Acquisitions                                     29\n",
      "Electronic Resources                             27\n",
      "Library Systems (e.g. Discovery or ILS support)  22\n",
      "Collection Development                           21\n",
      "Interlibrary Loan                                19\n",
      "Archives and/or Special Collections              12\n",
      "Federal Depository Library Program               11\n",
      "Other                                             7\n",
      "nan                                               1\n"
     ]
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "other['Q5'] = other['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "other_areas=other['Q5'].str.split(',', expand=True).stack().value_counts().to_frame()\n",
    "print(other_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "228ee927",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NaN Value Excluded\n",
    "\n",
    "other_df={'Function':['Cataloging', 'Processing', 'Acquisitions',\n",
    "                    'Electronic Resources', 'Library Systems', 'Collection Development',\n",
    "                    'Interlibrary Loan', 'Archives and/or Special Collections',\n",
    "                    'Federal Depository Library Program', 'Other'],\n",
    "        'Number of Units Including Functions': [44, 37, 29, 27, 22, 21, 19, 12, 11, 7]}\n",
    "\n",
    "x=other_df['Function']\n",
    "y=other_df['Number of Units Including Functions']\n",
    "labels=['Cataloging', 'Processing', 'Acquisitions',\n",
    "        'Electronic Resources', 'Library Systems',\n",
    "        'Collection Development','Interlibrary Loan', \n",
    "        'Archives and/or Special Collections',\n",
    "        'Federal Depository Library Program', 'Other']\n",
    "\n",
    "plt.pie(y, labels=labels, autopct='%1.1f%%')\n",
    "plt.title('Technical Services Participation by Other Libraries')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Other_Services.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e6ef49cb",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Q1         Q2                    Q3  Q4  \\\n",
      "0  Yes  As-Needed  Three Months or less  No   \n",
      "1  Yes  As-Needed        1 Year or more  No   \n",
      "2  Yes        Yes            4-6 Months  No   \n",
      "3  Yes         No  Three Months or less  No   \n",
      "4  Yes        Yes        1 Year or more  No   \n",
      "\n",
      "                                                  Q5    Q6    Q7  \\\n",
      "0                                         Cataloging  11.0  10.0   \n",
      "1  Cataloging,Processing,Electronic Resources,Int...   7.0   2.0   \n",
      "2  Cataloging,Processing,Electronic Resources,Int...   NaN   NaN   \n",
      "3  Cataloging,Processing,Acquisitions,Federal Dep...   1.0   1.0   \n",
      "4  Cataloging,Processing,Acquisitions,Library Sys...   4.0   4.0   \n",
      "\n",
      "                                                  Q8   Q9             Q10  \\\n",
      "0  We did not temporarily or permanently lose sta...  NaN    I'm not sure   \n",
      "1          Lay-Offs,Voluntary Retirement,Resignation  Yes             Yes   \n",
      "2  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "3                                     Not applicable  NaN  Not applicable   \n",
      "4  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "\n",
      "   ...                                                Q29 Q30  \\\n",
      "0  ...                                                NaN NaN   \n",
      "1  ...  Change to a workflow,Change in organizational ... NaN   \n",
      "2  ...                                                NaN NaN   \n",
      "3  ...                                                NaN NaN   \n",
      "4  ...                                                NaN NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0                                     Not applicable   \n",
      "1  I somewhat agree that these changes have been ...   \n",
      "2                                                NaN   \n",
      "3  I somewhat agree that these changes have been ...   \n",
      "4                                                NaN   \n",
      "\n",
      "                                                 Q32  \\\n",
      "0                                     Not applicable   \n",
      "1  I somewhat agree that these changes will have ...   \n",
      "2                                                NaN   \n",
      "3  I somewhat agree that these changes will have ...   \n",
      "4                                                NaN   \n",
      "\n",
      "                             Q33  \\\n",
      "0                 Not applicable   \n",
      "1              Somewhat positive   \n",
      "2                            NaN   \n",
      "3  Neither positive nor negative   \n",
      "4                            NaN   \n",
      "\n",
      "                                                 Q34  Q35  Q36 Q37  Q38  \n",
      "0                                                NaN   No  NaN NaN  NaN  \n",
      "1                                                NaN   No  NaN NaN  NaN  \n",
      "2                                                NaN  NaN  NaN NaN  NaN  \n",
      "3  As long as there are donations to our special ...   No  NaN NaN  NaN  \n",
      "4                                                NaN  NaN  NaN NaN  NaN  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "           Q1  Q2                    Q3            Q4  \\\n",
      "41  As-Needed  No  Three Months or less            No   \n",
      "42         No  No        Not applicable            No   \n",
      "43        Yes  No            4-6 Months            No   \n",
      "44        Yes  No  Three Months or less            No   \n",
      "45        Yes  No            4-6 Months  I'm Not Sure   \n",
      "\n",
      "                                                   Q5   Q6   Q7  \\\n",
      "41       Processing,Electronic Resources,Acquisitions  1.0  1.0   \n",
      "42  Cataloging,Processing,Electronic Resources,Int...  2.0  2.0   \n",
      "43    Library Systems (e.g. Discovery or ILS support)  1.0  1.0   \n",
      "44  Cataloging,Processing,Electronic Resources,Acq...  4.0  4.0   \n",
      "45  Cataloging,Processing,Electronic Resources,Int...  1.0  1.0   \n",
      "\n",
      "                                                   Q8   Q9             Q10  \\\n",
      "41                                     Not applicable  NaN              No   \n",
      "42  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "43  We did not temporarily or permanently lose sta...   No              No   \n",
      "44  We did not temporarily or permanently lose sta...  NaN    I'm not sure   \n",
      "45                                     Not applicable  NaN  Not applicable   \n",
      "\n",
      "    ...                                     Q29 Q30  \\\n",
      "41  ...                                     NaN NaN   \n",
      "42  ...                                     NaN NaN   \n",
      "43  ...  Change to Cataloging and/or Processing NaN   \n",
      "44  ...                                     NaN NaN   \n",
      "45  ...                                     NaN NaN   \n",
      "\n",
      "                                                  Q31  \\\n",
      "41                                                NaN   \n",
      "42                                                NaN   \n",
      "43  I neither agree nor disagree about the success...   \n",
      "44                                                NaN   \n",
      "45                                                NaN   \n",
      "\n",
      "                                                  Q32                 Q33  \\\n",
      "41                                                NaN                 NaN   \n",
      "42                                                NaN                 NaN   \n",
      "43  I somewhat disagree that these changes will ha...  Extremely positive   \n",
      "44                                                NaN                 NaN   \n",
      "45                                                NaN                 NaN   \n",
      "\n",
      "    Q34  Q35  Q36 Q37  Q38  \n",
      "41  NaN  NaN  NaN NaN  NaN  \n",
      "42  NaN  NaN  NaN NaN  NaN  \n",
      "43  NaN  NaN  NaN NaN  NaN  \n",
      "44  NaN  NaN  NaN NaN  NaN  \n",
      "45  NaN  NaN  NaN NaN  NaN  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Blanks\n",
    "\n",
    "#Import File\n",
    "blanks=pd.read_csv('Impact_of_COVID_on_Tech_Services_Blank_Types.csv')\n",
    "\n",
    "#Data Analysis\n",
    "print(blanks.head()) #Column names, data samples\n",
    "print(blanks.tail()) #Tail sample, 46 rows\n",
    "blanks.describe() #5 rows with 38 columns\n",
    "type(blanks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8bd71d8f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q6    Q7\n",
       "0   11.0  10.0\n",
       "1    7.0   2.0\n",
       "3    1.0   1.0\n",
       "4    4.0   4.0\n",
       "5    3.0   2.0\n",
       "6    7.0   7.0\n",
       "7    1.0   1.0\n",
       "9    2.0   2.0\n",
       "10   2.0   1.0\n",
       "11  10.0  10.0\n",
       "12   2.0   2.0\n",
       "13  11.5   7.0\n",
       "14   7.0   5.0\n",
       "15   2.0   2.0\n",
       "16   1.0   1.0\n",
       "18   1.0   1.0\n",
       "20   5.0   6.0\n",
       "21   3.0   3.0\n",
       "22  12.0   7.0\n",
       "23   3.0   3.0\n",
       "24   4.0   4.0\n",
       "25   1.0   1.0\n",
       "26   1.0   1.0\n",
       "27   2.0   2.0\n",
       "28   1.0   1.0\n",
       "29   1.0   1.0\n",
       "30   1.0   1.0\n",
       "31   1.0   1.0\n",
       "36   6.0   5.0\n",
       "37  10.0  10.0\n",
       "38   2.0   2.0\n",
       "40  12.0  12.0\n",
       "41   1.0   1.0\n",
       "42   2.0   2.0\n",
       "43   1.0   1.0\n",
       "44   4.0   4.0\n",
       "45   1.0   1.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Staffing Numbers Before and After\n",
    "blanks_staffing=blanks[['Q6','Q7']]\n",
    "blanks_staffing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2b3623b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9594594594594597\n",
      "3.4324324324324325\n",
      "146.5\n",
      "127.0\n"
     ]
    }
   ],
   "source": [
    "#Average\n",
    "print(blanks_staffing['Q6'].mean())\n",
    "print(blanks_staffing['Q7'].mean())\n",
    "\n",
    "# Before: 4.23\n",
    "# After: 3.76\n",
    "# Percent Change: 11.11% Decrease\n",
    "\n",
    "#Sum\n",
    "print(blanks_staffing['Q6'].sum())\n",
    "print(blanks_staffing['Q7'].sum())\n",
    "\n",
    "# Before: 162.5\n",
    "# After: 143.0\n",
    "# Difference: 19.5 positions lost\n",
    "\n",
    "# Post Data Restructure\n",
    "# Before: 3.96\n",
    "# After: 3.43\n",
    "# Percent Change: 13.39% Decrease\n",
    "\n",
    "# Positions Lost\n",
    "# Before: 146.5\n",
    "# After: 127.0\n",
    "# Difference: 19.5 positions lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d292827b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  0\n",
      "Cataloging                                       39\n",
      "Processing                                       34\n",
      "Acquisitions                                     28\n",
      "Electronic Resources                             26\n",
      "Collection Development                           24\n",
      "Library Systems (e.g. Discovery or ILS support)  21\n",
      "Interlibrary Loan                                13\n",
      "Archives and/or Special Collections               8\n",
      "Other                                             6\n",
      "Federal Depository Library Program                3\n",
      "nan                                               2\n"
     ]
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "blanks['Q5'] = blanks['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "blanks_areas=blanks['Q5'].str.split(',', expand=True).stack().value_counts().to_frame()\n",
    "print(blanks_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d2f85b87",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NaN Value Excluded\n",
    "\n",
    "blanks_df={'Function':['Cataloging', 'Processing', 'Acquisitions',\n",
    "                       'Electronic Resources', 'Collection Development',\n",
    "                       'Library Systems', 'Interlibrary Loan', 'Archives and/or Special Collections',\n",
    "                       'Other', 'Federal Depository Library Program'],\n",
    "           'Number of Units Including Functions': [39, 34, 28, 26, 24, 21, 13, 8, 6, 3]}\n",
    "\n",
    "x=blanks_df['Function']\n",
    "y=blanks_df['Number of Units Including Functions']\n",
    "labels=['Cataloging', 'Processing', 'Acquisitions',\n",
    "        'Electronic Resources', 'Collection Development',\n",
    "        'Library Systems', 'Interlibrary Loan', 'Archives and/or Special Collections',\n",
    "        'Other', 'Federal Depository Library Program']\n",
    "\n",
    "plt.pie(y, labels=labels, autopct='%1.1f%%')\n",
    "plt.title('Technical Services Participation by Blank Response Libraries')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Blanks_Services.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
